From 69bcdda5b14f59394a5220d0a836fd0391957978 Mon Sep 17 00:00:00 2001
From: levenc <cyril.leven@chu-brest.fr>
Date: Tue, 27 Aug 2024 14:33:17 +0200
Subject: [PATCH] Upgrade the version number to 1.2.5

---
 DESCRIPTION                                 |   2 +-
 NEWS.md                                     |   2 +-
 docs/404.html                               |   2 +-
 docs/LICENSE.html                           |   2 +-
 docs/articles/a_posteriori_dosing.html      |   2 +-
 docs/articles/a_priori_dosing.html          |   2 +-
 docs/articles/auc_based_dosing.html         |   2 +-
 docs/articles/classic_posologyr_models.html |   2 +-
 docs/articles/index.html                    |   2 +-
 docs/articles/multiple_endpoints.html       |   2 +-
 docs/articles/patient_data.html             |   2 +-
 docs/articles/population_models.html        |   2 +-
 docs/articles/route_of_administration.html  |   2 +-
 docs/authors.html                           |   2 +-
 docs/index.html                             |   2 +-
 docs/news/index.html                        |  10 +-
 docs/pkgdown.yml                            |   2 +-
 docs/reference/index.html                   |   2 +-
 docs/reference/poso_dose_auc.html           |   2 +-
 docs/reference/poso_dose_conc.html          |   2 +-
 docs/reference/poso_estim_map.html          |   2 +-
 docs/reference/poso_estim_mcmc.html         |   2 +-
 docs/reference/poso_estim_sir.html          |   2 +-
 docs/reference/poso_inter_cmin.html         |   8 +-
 docs/reference/poso_simu_pop.html           | 246 ++++++++++----------
 docs/reference/poso_time_cmin.html          |   6 +-
 docs/reference/posologyr_error_lines.html   |   2 +-
 docs/search.json                            |   2 +-
 28 files changed, 159 insertions(+), 159 deletions(-)

diff --git a/DESCRIPTION b/DESCRIPTION
index 9af092c..fe5bb20 100644
--- a/DESCRIPTION
+++ b/DESCRIPTION
@@ -1,6 +1,6 @@
 Package: posologyr
 Title: Individual Dose Optimization using Population Pharmacokinetics
-Version: 1.2.4.9000
+Version: 1.2.5
 Authors@R: c(
     person("Cyril","Leven",role = c("aut", "cre","cph"),
            email = "cyril.leven@chu-brest.fr",
diff --git a/NEWS.md b/NEWS.md
index c24347c..49a05ee 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,4 +1,4 @@
-# posologyr v1.2.4.9000
+# posologyr v1.2.5
 
 ## Additional feature
 * The route of administration (i.e. the compartment in which the drug is to be administered) can now be specified in `poso_time_cmin()`, `poso_dose_conc()`, `poso_dose_auc()` and `poso_inter_cmin()`.
diff --git a/docs/404.html b/docs/404.html
index cf82acb..1534146 100644
--- a/docs/404.html
+++ b/docs/404.html
@@ -27,7 +27,7 @@
 
     <a class="navbar-brand me-2" href="https://levenc.github.io/posologyr/index.html">posologyr</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.4.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.5</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/LICENSE.html b/docs/LICENSE.html
index 096212d..14932ee 100644
--- a/docs/LICENSE.html
+++ b/docs/LICENSE.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="index.html">posologyr</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.4.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.5</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/articles/a_posteriori_dosing.html b/docs/articles/a_posteriori_dosing.html
index 765bc9a..f9a1547 100644
--- a/docs/articles/a_posteriori_dosing.html
+++ b/docs/articles/a_posteriori_dosing.html
@@ -26,7 +26,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">posologyr</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.4.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.5</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/articles/a_priori_dosing.html b/docs/articles/a_priori_dosing.html
index 1a11615..77e8bcb 100644
--- a/docs/articles/a_priori_dosing.html
+++ b/docs/articles/a_priori_dosing.html
@@ -26,7 +26,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">posologyr</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.4.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.5</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/articles/auc_based_dosing.html b/docs/articles/auc_based_dosing.html
index 28d6a4a..6af6639 100644
--- a/docs/articles/auc_based_dosing.html
+++ b/docs/articles/auc_based_dosing.html
@@ -26,7 +26,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">posologyr</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.4.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.5</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/articles/classic_posologyr_models.html b/docs/articles/classic_posologyr_models.html
index e8a525c..ea7c7ba 100644
--- a/docs/articles/classic_posologyr_models.html
+++ b/docs/articles/classic_posologyr_models.html
@@ -26,7 +26,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">posologyr</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.4.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.5</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/articles/index.html b/docs/articles/index.html
index dfbfc94..8ac7d1a 100644
--- a/docs/articles/index.html
+++ b/docs/articles/index.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">posologyr</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.4.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.5</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/articles/multiple_endpoints.html b/docs/articles/multiple_endpoints.html
index f175bfb..0b28531 100644
--- a/docs/articles/multiple_endpoints.html
+++ b/docs/articles/multiple_endpoints.html
@@ -26,7 +26,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">posologyr</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.4.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.5</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/articles/patient_data.html b/docs/articles/patient_data.html
index 65194ce..0006d32 100644
--- a/docs/articles/patient_data.html
+++ b/docs/articles/patient_data.html
@@ -26,7 +26,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">posologyr</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.4.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.5</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/articles/population_models.html b/docs/articles/population_models.html
index 4eda87c..2c1d455 100644
--- a/docs/articles/population_models.html
+++ b/docs/articles/population_models.html
@@ -26,7 +26,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">posologyr</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.4.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.5</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/articles/route_of_administration.html b/docs/articles/route_of_administration.html
index c6ce21e..5a2f8de 100644
--- a/docs/articles/route_of_administration.html
+++ b/docs/articles/route_of_administration.html
@@ -26,7 +26,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">posologyr</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.4.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.5</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/authors.html b/docs/authors.html
index 4c78e5b..1ec7c4c 100644
--- a/docs/authors.html
+++ b/docs/authors.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="index.html">posologyr</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.4.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.5</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/index.html b/docs/index.html
index 500c54c..88added 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -29,7 +29,7 @@
 
     <a class="navbar-brand me-2" href="index.html">posologyr</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.4.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.5</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/news/index.html b/docs/news/index.html
index 041d5aa..bfe93cf 100644
--- a/docs/news/index.html
+++ b/docs/news/index.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">posologyr</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.4.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.5</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -49,15 +49,15 @@
     </div>
 
     <div class="section level2">
-<h2 class="pkg-version" data-toc-text="1.2.4.9000" id="posologyr-v1249000">posologyr v1.2.4.9000<a class="anchor" aria-label="anchor" href="#posologyr-v1249000"></a></h2>
+<h2 class="pkg-version" data-toc-text="1.2.5" id="posologyr-v125">posologyr v1.2.5<a class="anchor" aria-label="anchor" href="#posologyr-v125"></a></h2>
 <div class="section level3">
-<h3 id="additional-feature-1-2-4-9000">Additional feature<a class="anchor" aria-label="anchor" href="#additional-feature-1-2-4-9000"></a></h3>
+<h3 id="additional-feature-1-2-5">Additional feature<a class="anchor" aria-label="anchor" href="#additional-feature-1-2-5"></a></h3>
 <ul><li>The route of administration (i.e. the compartment in which the drug is to be administered) can now be specified in <code><a href="../reference/poso_time_cmin.html">poso_time_cmin()</a></code>, <code><a href="../reference/poso_dose_conc.html">poso_dose_conc()</a></code>, <code><a href="../reference/poso_dose_auc.html">poso_dose_auc()</a></code> and <code><a href="../reference/poso_inter_cmin.html">poso_inter_cmin()</a></code>.</li>
 <li>
 <code><a href="../reference/poso_simu_pop.html">poso_simu_pop()</a></code> provides an rxode2 model using the simulated ETA and the input dataset, with interpolation of covariates, to make plotting easier</li>
 </ul></div>
 <div class="section level3">
-<h3 id="documentation-1-2-4-9000">Documentation<a class="anchor" aria-label="anchor" href="#documentation-1-2-4-9000"></a></h3>
+<h3 id="documentation-1-2-5">Documentation<a class="anchor" aria-label="anchor" href="#documentation-1-2-5"></a></h3>
 <ul><li>The README illustrates a simple example of dose adaptation</li>
 <li>
 <code><a href="../articles/route_of_administration.html">vignette("route_of_administration")</a></code> shows how to select a route of administration for optimal dosing</li>
@@ -70,7 +70,7 @@ <h3 id="documentation-1-2-4-9000">Documentation<a class="anchor" aria-label="anc
 <li>Examples use <code>rxode2</code> model functions</li>
 </ul></div>
 <div class="section level3">
-<h3 id="bug-fix-1-2-4-9000">Bug fix<a class="anchor" aria-label="anchor" href="#bug-fix-1-2-4-9000"></a></h3>
+<h3 id="bug-fix-1-2-5">Bug fix<a class="anchor" aria-label="anchor" href="#bug-fix-1-2-5"></a></h3>
 <ul><li>Fix a bug where <code><a href="../reference/poso_estim_map.html">poso_estim_map()</a></code>, <code><a href="../reference/poso_estim_sir.html">poso_estim_sir()</a></code> and <code><a href="../reference/poso_simu_pop.html">poso_simu_pop()</a></code> failed for models featuring a single parameter with IIV.</li>
 </ul></div>
 </div>
diff --git a/docs/pkgdown.yml b/docs/pkgdown.yml
index 4d1b80a..410819d 100644
--- a/docs/pkgdown.yml
+++ b/docs/pkgdown.yml
@@ -10,7 +10,7 @@ articles:
   patient_data: patient_data.html
   population_models: population_models.html
   route_of_administration: route_of_administration.html
-last_built: 2024-08-27T08:13Z
+last_built: 2024-08-27T12:29Z
 urls:
   reference: https://levenc.github.io/posologyr/reference
   article: https://levenc.github.io/posologyr/articles
diff --git a/docs/reference/index.html b/docs/reference/index.html
index dbb8633..9fa6ad8 100644
--- a/docs/reference/index.html
+++ b/docs/reference/index.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">posologyr</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.4.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.5</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/poso_dose_auc.html b/docs/reference/poso_dose_auc.html
index 27a5ed5..dd649cd 100644
--- a/docs/reference/poso_dose_auc.html
+++ b/docs/reference/poso_dose_auc.html
@@ -11,7 +11,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">posologyr</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.4.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.5</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/poso_dose_conc.html b/docs/reference/poso_dose_conc.html
index 62d6d54..42c6171 100644
--- a/docs/reference/poso_dose_conc.html
+++ b/docs/reference/poso_dose_conc.html
@@ -11,7 +11,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">posologyr</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.4.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.5</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/poso_estim_map.html b/docs/reference/poso_estim_map.html
index 70be92e..e48d198 100644
--- a/docs/reference/poso_estim_map.html
+++ b/docs/reference/poso_estim_map.html
@@ -9,7 +9,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">posologyr</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.4.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.5</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/poso_estim_mcmc.html b/docs/reference/poso_estim_mcmc.html
index 0492727..002c67f 100644
--- a/docs/reference/poso_estim_mcmc.html
+++ b/docs/reference/poso_estim_mcmc.html
@@ -9,7 +9,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">posologyr</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.4.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.5</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/poso_estim_sir.html b/docs/reference/poso_estim_sir.html
index 4cf99fd..4dbe615 100644
--- a/docs/reference/poso_estim_sir.html
+++ b/docs/reference/poso_estim_sir.html
@@ -9,7 +9,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">posologyr</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.4.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.5</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/reference/poso_inter_cmin.html b/docs/reference/poso_inter_cmin.html
index 8d4ac1c..76cba17 100644
--- a/docs/reference/poso_inter_cmin.html
+++ b/docs/reference/poso_inter_cmin.html
@@ -11,7 +11,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">posologyr</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.4.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.5</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -230,7 +230,7 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $interval</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 17.76029</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> [1] 17.76028</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $type_of_estimate</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] "point estimate"</span>
@@ -239,8 +239,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] 2.500425</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $indiv_param</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   THETA_Cl THETA_Vc THETA_Ka   prop.sd    ETA_Cl    ETA_Vc    ETA_Ka</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1        4       70        1 0.2236068 0.6019038 -0.429173 0.1278482</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   THETA_Cl THETA_Vc THETA_Ka   prop.sd    ETA_Cl     ETA_Vc    ETA_Ka</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1        4       70        1 0.2236068 0.6019035 -0.4291739 0.1278471</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
diff --git a/docs/reference/poso_simu_pop.html b/docs/reference/poso_simu_pop.html
index d938164..fe78d36 100644
--- a/docs/reference/poso_simu_pop.html
+++ b/docs/reference/poso_simu_pop.html
@@ -9,7 +9,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">posologyr</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.4.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.5</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -143,124 +143,124 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-msg co"><span class="r-pr">#&gt;</span>  </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $eta</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>           ETA_Cl       ETA_Vc       ETA_Ka</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1   -0.626118495  0.114181258 -1.089977423</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 2   -0.002491555  0.277966827  0.513585283</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 3   -0.814741627 -0.110607238 -0.109209384</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 4   -0.126429720 -0.247621892  0.281289321</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 5    0.923507208 -0.729400635  0.229164299</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 6   -0.833164068 -0.233451094 -0.023524289</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 7    0.242835347 -0.408786690  0.209365022</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 8    0.162316736 -0.583409609  0.329943601</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 9    0.844565080 -0.043578776 -0.418523660</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 10  -0.007133196 -0.369751261 -0.676365686</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 11   0.418307135  0.078928106  0.108979453</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 12   0.726073134  0.050104954 -0.059925286</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 13  -0.854217084 -0.124878691 -0.140177303</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 14   0.477314594  0.031320537 -0.285824640</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 15  -0.022344982 -0.112466815  0.198919317</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 16   1.232260201  0.020809466  0.258359350</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 17   0.052858355 -0.854947394  0.385536795</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 18  -0.108778777 -0.092164995  0.008576480</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 19   0.013219971  0.245890352 -1.017015082</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 20   1.199676043 -0.161543056  0.095415592</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 21   0.480462085 -0.297436507  0.498174667</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 22  -0.109968218 -0.526622321 -0.436413663</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 23   0.476308114  0.058884898  0.218521447</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 24  -0.760017399 -0.657733272  0.127075897</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 25   0.598067870  0.105853796  0.589558724</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 26   0.234299580  0.271345975 -0.049164727</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 27   0.077002004 -0.040395591  0.860592505</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 28   0.580658894  0.334869635  0.248751082</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 29  -0.245188102  0.496646302 -1.168271430</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 30  -0.069628373  0.194041413 -0.170813730</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 31   0.189702450  0.475433666  0.468998541</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 32  -0.017040133  0.217412406  0.748135847</span>
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-<span class="r-out co"><span class="r-pr">#&gt;</span> 84   0.057656072 -0.371283104 -0.225213596</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 85  -0.533812565 -0.336180890  0.651072068</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 86  -0.370562765  0.129591078 -0.214686445</span>
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-<span class="r-out co"><span class="r-pr">#&gt;</span> 93   0.965425280  0.317393983  0.343005395</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 94  -0.137836338  0.452580985 -0.411012369</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 95   0.251951230  0.144218670  0.163981760</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 96   0.505277641 -0.421050740  0.097419956</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 97   0.632991621 -0.171610636 -0.077853793</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 98  -0.099167163 -0.451474970  0.214986875</span>
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+<span class="r-out co"><span class="r-pr">#&gt;</span>          ETA_Cl        ETA_Vc       ETA_Ka</span>
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+<span class="r-out co"><span class="r-pr">#&gt;</span> 6    0.08829972  1.0646795643  0.177153842</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 7   -0.56754035 -0.3004684498  0.086744472</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 8   -0.17353057  0.0297399528 -0.239102222</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 9   -0.37204606 -0.0681723758  0.667158179</span>
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+<span class="r-out co"><span class="r-pr">#&gt;</span> 47   0.12095204  0.1063290947  1.054290892</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 48  -0.65677056  0.6579158907  0.208758285</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 49  -0.01757885 -0.0588808083  0.371926925</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 50  -0.24673067 -0.1805240163 -0.097830097</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 51   0.39191046  0.4159538681 -0.037967541</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 52  -0.87001416 -0.0101960308 -0.244342202</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 53   0.69353025  0.3160638259  0.007746790</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 54   0.76718629 -0.5305894330  0.687736930</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 55  -0.15600010 -0.5372649408  0.233664676</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 56  -0.22696478  0.3977303316 -0.325538825</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 57   0.34939402  0.3356843830 -0.460650352</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 58  -0.33446585 -0.4592726994 -0.762361942</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 59   0.56771730 -0.0018453990  0.164049535</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 60   0.02221040 -0.2074472251 -0.245654268</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 61  -0.43756583  0.4004970612 -0.323059623</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 62   0.22864983 -0.1643911881 -0.385340511</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 63   0.04960210 -0.9753202418 -0.156391704</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 64   0.41269427  0.9290324438  0.488513823</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 65   0.48279891  0.5271998013 -0.145369898</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 66  -0.01509115 -0.3794736224  0.633903308</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 67   0.07928208  0.2879443073 -0.147487550</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 68   0.20664080  0.0326334612  0.362415054</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 69   0.06363990  0.8187491113  0.398934107</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 70  -0.06412535  0.4812014182 -0.191628561</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 71   0.44473418  0.3355002381 -0.190077991</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 72  -0.35786214 -0.6259152421  0.309481419</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 73   0.39099910 -0.8330321749  0.258486502</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 74   0.60591300  0.7522020684  0.003734992</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 75   0.14105591 -0.0772794560  0.199033893</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 76   0.11255801  0.0686682135 -0.014146813</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 77   0.29403252 -0.7716550381 -0.377551574</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 78  -0.10415015 -0.4098232879 -0.277269197</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 79   0.04687274  0.3381934530  0.466881915</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 80  -0.01657469 -0.0004508222 -0.274059996</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 81   0.34703845 -0.1353410256 -0.518273028</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 82  -0.77759568  0.0783792867 -0.009475904</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 83   0.09837731 -0.2966746333 -0.151063236</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 84   0.86918648  0.5425733777 -0.484762337</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 85  -0.20056939 -0.0337251850  0.880821557</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 86   0.22423405  0.2266253611  0.098053962</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 87  -0.06289958 -0.0805403600 -0.328896523</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 88   0.91974567  1.2345015201  1.176161346</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 89   0.87918576  0.2155617721  0.643564140</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 90  -0.82927912 -0.3592766936 -0.700110707</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 91  -0.34914053  0.0990080614 -0.229746377</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 92  -0.22935254 -0.0841394013 -0.248344505</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 93  -0.22056017  0.1123495210  0.058659532</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 94  -0.24618351  0.1502172258 -0.043350296</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 95   0.61466996  0.3262538319 -0.020061632</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 96  -0.26323832 -0.1269916789  0.930418768</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 97   0.73455148 -0.0915474373  0.805957437</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 98  -0.03921193  0.1359026367  0.274076390</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 99   0.14639569 -0.1606572049 -0.790413751</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 100  0.08872456  0.5419509531 -0.251199157</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $model</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="font-weight: bold;">── Solved rxode2 object ──</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="font-weight: bold;">── Parameters (</span><span style="color: #0000BB; font-weight: bold;">$params</span><span style="font-weight: bold;">): ──</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 100 × 8</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    sim.id THETA_Cl THETA_Vc THETA_Ka prop.sd   ETA_Cl  ETA_Vc  ETA_Ka</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;int&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span>      1        4       70        1   0.224 -<span style="color: #BB0000;">0.626</span>    0.114  -<span style="color: #BB0000;">1.09</span>  </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span>      2        4       70        1   0.224 -<span style="color: #BB0000;">0.002</span><span style="color: #BB0000; text-decoration: underline;">49</span>  0.278   0.514 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span>      3        4       70        1   0.224 -<span style="color: #BB0000;">0.815</span>   -<span style="color: #BB0000;">0.111</span>  -<span style="color: #BB0000;">0.109</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span>      4        4       70        1   0.224 -<span style="color: #BB0000;">0.126</span>   -<span style="color: #BB0000;">0.248</span>   0.281 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span>      5        4       70        1   0.224  0.924   -<span style="color: #BB0000;">0.729</span>   0.229 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span>      6        4       70        1   0.224 -<span style="color: #BB0000;">0.833</span>   -<span style="color: #BB0000;">0.233</span>  -<span style="color: #BB0000;">0.023</span><span style="color: #BB0000; text-decoration: underline;">5</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span>      7        4       70        1   0.224  0.243   -<span style="color: #BB0000;">0.409</span>   0.209 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span>      8        4       70        1   0.224  0.162   -<span style="color: #BB0000;">0.583</span>   0.330 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span>      9        4       70        1   0.224  0.845   -<span style="color: #BB0000;">0.043</span><span style="color: #BB0000; text-decoration: underline;">6</span> -<span style="color: #BB0000;">0.419</span> </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span>     10        4       70        1   0.224 -<span style="color: #BB0000;">0.007</span><span style="color: #BB0000; text-decoration: underline;">13</span> -<span style="color: #BB0000;">0.370</span>  -<span style="color: #BB0000;">0.676</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    sim.id THETA_Cl THETA_Vc THETA_Ka prop.sd  ETA_Cl  ETA_Vc  ETA_Ka</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>     <span style="color: #949494; font-style: italic;">&lt;int&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>   <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 1</span>      1        4       70        1   0.224  0.128  -<span style="color: #BB0000;">0.847</span>   0.973 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 2</span>      2        4       70        1   0.224  0.150  -<span style="color: #BB0000;">0.617</span>  -<span style="color: #BB0000;">0.288</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 3</span>      3        4       70        1   0.224 -<span style="color: #BB0000;">0.263</span>   0.591  -<span style="color: #BB0000;">1.38</span>  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 4</span>      4        4       70        1   0.224  0.063<span style="text-decoration: underline;">6</span> -<span style="color: #BB0000;">1.05</span>    0.292 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 5</span>      5        4       70        1   0.224 -<span style="color: #BB0000;">0.047</span><span style="color: #BB0000; text-decoration: underline;">4</span>  0.302   0.017<span style="text-decoration: underline;">6</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 6</span>      6        4       70        1   0.224  0.088<span style="text-decoration: underline;">3</span>  1.06    0.177 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 7</span>      7        4       70        1   0.224 -<span style="color: #BB0000;">0.568</span>  -<span style="color: #BB0000;">0.300</span>   0.086<span style="text-decoration: underline;">7</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 8</span>      8        4       70        1   0.224 -<span style="color: #BB0000;">0.174</span>   0.029<span style="text-decoration: underline;">7</span> -<span style="color: #BB0000;">0.239</span> </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;"> 9</span>      9        4       70        1   0.224 -<span style="color: #BB0000;">0.372</span>  -<span style="color: #BB0000;">0.068</span><span style="color: #BB0000; text-decoration: underline;">2</span>  0.667 </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">10</span>     10        4       70        1   0.224  0.133   0.028<span style="text-decoration: underline;">4</span>  0.182 </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 90 more rows</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="font-weight: bold;">── Initial Conditions (</span><span style="color: #0000BB; font-weight: bold;">$inits</span><span style="font-weight: bold;">): ──</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> depot centr   AUC </span>
@@ -270,16 +270,16 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="font-weight: bold;">── First part of data (object): ──</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># A tibble: 15,100 × 14</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   sim.id  time  TVCl  TVVc  TVKa    Cl    Vc    Ka    K20   rxCc     Cc depot</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;int&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>  <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>  <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span>  <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span>      1   0       4    70     1  2.14  78.5 0.336 0.027<span style="text-decoration: underline;">3</span> 0      0         0 </span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span>      1   0.1     4    70     1  2.14  78.5 0.336 0.027<span style="text-decoration: underline;">3</span> 0.084<span style="text-decoration: underline;">7</span> 0.084<span style="text-decoration: underline;">7</span>  393.</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">3</span>      1   0.2     4    70     1  2.14  78.5 0.336 0.027<span style="text-decoration: underline;">3</span> 0.335  0.335   774.</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">4</span>      1   0.3     4    70     1  2.14  78.5 0.336 0.027<span style="text-decoration: underline;">3</span> 0.744  0.744  <span style="text-decoration: underline;">1</span>141.</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">5</span>      1   0.4     4    70     1  2.14  78.5 0.336 0.027<span style="text-decoration: underline;">3</span> 1.31   1.31   <span style="text-decoration: underline;">1</span>497.</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">6</span>      1   0.5     4    70     1  2.14  78.5 0.336 0.027<span style="text-decoration: underline;">3</span> 2.02   2.02   <span style="text-decoration: underline;">1</span>841.</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   sim.id  time  TVCl  TVVc  TVKa    Cl    Vc    Ka   K20  rxCc    Cc depot centr</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>    <span style="color: #949494; font-style: italic;">&lt;int&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span> <span style="color: #949494; font-style: italic;">&lt;dbl&gt;</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">1</span>      1   0       4    70     1  4.55  30.0  2.65 0.152  0     0       0    0  </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">2</span>      1   0.1     4    70     1  4.55  30.0  2.65 0.152  1.61  1.61  351.  48.3</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">3</span>      1   0.2     4    70     1  4.55  30.0  2.65 0.152  5.90  5.90  621. 177. </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">4</span>      1   0.3     4    70     1  4.55  30.0  2.65 0.152 12.2  12.2   828. 366. </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">5</span>      1   0.4     4    70     1  4.55  30.0  2.65 0.152 20.0  20.0   987. 600. </span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #BCBCBC;">6</span>      1   0.5     4    70     1  4.55  30.0  2.65 0.152 28.9  28.9  <span style="text-decoration: underline;">1</span>109. 867. </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 15,094 more rows</span></span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 2 more variables: centr &lt;dbl&gt;, AUC &lt;dbl&gt;</span></span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> <span style="color: #949494;"># ℹ 1 more variable: AUC &lt;dbl&gt;</span></span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $event</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> <span style="font-weight: bold;">── EventTable with 152 records ──</span></span>
diff --git a/docs/reference/poso_time_cmin.html b/docs/reference/poso_time_cmin.html
index 08d0203..2d10b9c 100644
--- a/docs/reference/poso_time_cmin.html
+++ b/docs/reference/poso_time_cmin.html
@@ -11,7 +11,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">posologyr</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.4.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.5</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
@@ -276,8 +276,8 @@ <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-e
 <span class="r-out co"><span class="r-pr">#&gt;</span> [1] 2.489933</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> $indiv_param</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span>   THETA_Cl THETA_Vc THETA_Ka   prop.sd    ETA_Cl     ETA_Vc    ETA_Ka</span>
-<span class="r-out co"><span class="r-pr">#&gt;</span> 1        4       70        1 0.2236068 0.6019036 -0.4291735 0.1278478</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span>   THETA_Cl THETA_Vc THETA_Ka   prop.sd    ETA_Cl     ETA_Vc   ETA_Ka</span>
+<span class="r-out co"><span class="r-pr">#&gt;</span> 1        4       70        1 0.2236068 0.6019037 -0.4291733 0.127848</span>
 <span class="r-out co"><span class="r-pr">#&gt;</span> </span>
 <span class="r-in"><span></span></span>
 </code></pre></div>
diff --git a/docs/reference/posologyr_error_lines.html b/docs/reference/posologyr_error_lines.html
index b3ad78d..3595cb0 100644
--- a/docs/reference/posologyr_error_lines.html
+++ b/docs/reference/posologyr_error_lines.html
@@ -7,7 +7,7 @@
 
     <a class="navbar-brand me-2" href="../index.html">posologyr</a>
 
-    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.4.9000</small>
+    <small class="nav-text text-muted me-auto" data-bs-toggle="tooltip" data-bs-placement="bottom" title="">1.2.5</small>
 
 
     <button class="navbar-toggler" type="button" data-bs-toggle="collapse" data-bs-target="#navbar" aria-controls="navbar" aria-expanded="false" aria-label="Toggle navigation">
diff --git a/docs/search.json b/docs/search.json
index f53631e..84639c9 100644
--- a/docs/search.json
+++ b/docs/search.json
@@ -1 +1 @@
-[{"path":"https://levenc.github.io/posologyr/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"GNU Affero General Public License","title":"GNU Affero General Public License","text":"Version 3, 19 November 2007 Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/> Everyone permitted copy distribute verbatim copies license document, changing allowed.","code":""},{"path":"https://levenc.github.io/posologyr/LICENSE.html","id":"preamble","dir":"","previous_headings":"","what":"Preamble","title":"GNU Affero General Public License","text":"GNU Affero General Public License free, copyleft license software kinds works, specifically designed ensure cooperation community case network server software. licenses software practical works designed take away freedom share change works. contrast, General Public Licenses intended guarantee freedom share change versions program–make sure remains free software users. speak free software, referring freedom, price. 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Definitions.","title":"GNU Affero General Public License","text":"“License” refers version 3 GNU Affero General Public License. “Copyright” also means copyright-like laws apply kinds works, semiconductor masks. “Program” refers copyrightable work licensed License. licensee addressed “”. “Licensees” “recipients” may individuals organizations. “modify” work means copy adapt part work fashion requiring copyright permission, making exact copy. resulting work called “modified version” earlier work work “based ” earlier work. “covered work” means either unmodified Program work based Program. “propagate” work means anything , without permission, make directly secondarily liable infringement applicable copyright law, except executing computer modifying private copy. Propagation includes copying, distribution (without modification), making available public, countries activities well. “convey” work means kind propagation enables parties make receive copies. 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Conveying Verbatim Copies.","title":"GNU Affero General Public License","text":"may convey verbatim copies Program’s source code receive , medium, provided conspicuously appropriately publish copy appropriate copyright notice; keep intact notices stating License non-permissive terms added accord section 7 apply code; keep intact notices absence warranty; give recipients copy License along Program. may charge price price copy convey, may offer support warranty protection fee.","code":""},{"path":"https://levenc.github.io/posologyr/LICENSE.html","id":"id_5-conveying-modified-source-versions","dir":"","previous_headings":"TERMS AND CONDITIONS","what":"5. 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Conveying Non-Source Forms.","title":"GNU Affero General Public License","text":"may convey covered work object code form terms sections 4 5, provided also convey machine-readable Corresponding Source terms License, one ways: Convey object code , embodied , physical product (including physical distribution medium), accompanied Corresponding Source fixed durable physical medium customarily used software interchange. Convey object code , embodied , physical product (including physical distribution medium), accompanied written offer, valid least three years valid long offer spare parts customer support product model, give anyone possesses object code either (1) copy Corresponding Source software product covered License, durable physical medium customarily used software interchange, price reasonable cost physically performing conveying source, (2) access copy Corresponding Source network server charge. 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Interpretation of Sections 15 and 16.","title":"GNU Affero General Public License","text":"disclaimer warranty limitation liability provided given local legal effect according terms, reviewing courts shall apply local law closely approximates absolute waiver civil liability connection Program, unless warranty assumption liability accompanies copy Program return fee. END TERMS CONDITIONS","code":""},{"path":"https://levenc.github.io/posologyr/articles/a_posteriori_dosing.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"A posteriori dose selection","text":"Dosage individualization critical care patient treated amikacin suspected ventilator-associated pneumonia, using population pharmacokinetic (ppk) model Burdet et al. 2015, using data therapeutic drug monitoring (TDM).","code":"mod_amikacin_Burdet2015 <- function() {     ini({       THETA_Cl=4.3       THETA_Vc=15.9       THETA_Vp=21.4       THETA_Q=12.1       ETA_Cl + ETA_Vc + ETA_Vp + ETA_Q ~         c(0.1,           0.01     ,   0.05 ,           0.01     ,   0.02 ,   0.2  ,           -0.06    ,   0.004,   0.003,    0.08)       add_sd <- 0.2       prop_sd <- 0.1     })     model({       TVCl  = THETA_Cl*(CLCREAT4H/82)^0.7       TVVc  = THETA_Vc*(TBW/78)^0.9*(PoverF/169)^0.4       TVVp  = THETA_Vp       TVQ   = THETA_Q       Cl    = TVCl*exp(ETA_Cl)       Vc    = TVVc*exp(ETA_Vc)       Vp    = TVVp*exp(ETA_Vp)       Q     = TVQ *exp(ETA_Q)       ke    = Cl/Vc       k12   = Q/Vc       k21   = Q/Vp       Cp    = centr/Vc       d/dt(centr)  = - ke*centr - k12*centr + k21*periph       d/dt(periph) =            + k12*centr - k21*periph        Cp ~ add(add_sd) + prop(prop_sd) + combined1()     })   }"},{"path":[]},{"path":"https://levenc.github.io/posologyr/articles/a_posteriori_dosing.html","id":"patient-record-with-tdm-data","dir":"Articles","previous_headings":"A posteriori dose selection","what":"Patient record with TDM data","title":"A posteriori dose selection","text":"first administration, dosage selection can refined using results TDM. See vignette(\"patient_data_input\") details regarding patient record. concentration measured 30 min 30 min infusion meet target peak concentration; < 60 mg/L.","code":"df_patientA <- data.frame(ID=1,TIME=c(0,1,6),                               DV=c(NA,58,14),                               EVID=c(1,0,0),                               AMT=c(2000,0,0),                               DUR=c(0.5,NA,NA),                               CLCREAT4H=50,TBW=62,PoverF=169) df_patientA #>   ID TIME DV EVID  AMT DUR CLCREAT4H TBW PoverF #> 1  1    0 NA    1 2000 0.5        50  62    169 #> 2  1    1 58    0    0  NA        50  62    169 #> 3  1    6 14    0    0  NA        50  62    169"},{"path":"https://levenc.github.io/posologyr/articles/a_posteriori_dosing.html","id":"estimate-the-map-individual-parameters","dir":"Articles","previous_headings":"A posteriori dose selection","what":"Estimate the MAP individual parameters","title":"A posteriori dose selection","text":"maximum posteriori (MAP) individual parameters estimated.","code":"patA_map <- poso_estim_map(dat=df_patientA,                            prior_model=mod_amikacin_Burdet2015)"},{"path":"https://levenc.github.io/posologyr/articles/a_posteriori_dosing.html","id":"plot-the-individual-pharmacokinetic-profile","dir":"Articles","previous_headings":"A posteriori dose selection","what":"Plot the individual pharmacokinetic profile","title":"A posteriori dose selection","text":"individual pharmacokinetic profile can plotted using rxode2 model provided poso_estim_map() function.","code":"plot(patA_map$model,Cc)"},{"path":"https://levenc.github.io/posologyr/articles/a_posteriori_dosing.html","id":"time-required-to-reach-the-target-cmin-following-the-first-administration","dir":"Articles","previous_headings":"A posteriori dose selection","what":"Time required to reach the target Cmin following the first administration","title":"A posteriori dose selection","text":"MAP estimates individual parameters, prediction time needed reaching target Cmin can updated. next dose (needed) can administered 33.9 hours following first infusion.","code":"poso_time_cmin(dat=df_patientA,                prior_model=mod_amikacin_Burdet2015,                tdm = TRUE,                target_cmin = 2.5) #> $time #> [1] 33.9 #>  #> $type_of_estimate #> [1] \"point estimate\" #>  #> $cmin_estimate #> [1] 2.487865 #>  #> $indiv_param #>   THETA_Cl THETA_Vc THETA_Vp THETA_Q add_sd prop_sd    ETA_Cl    ETA_Vc #> 3      4.3     15.9     21.4    12.1    0.2     0.1 0.4499479 0.2730561 #>      ETA_Vp    ETA_Q CLCREAT4H TBW PoverF #> 3 0.7061648 -0.13884        50  62    169"},{"path":"https://levenc.github.io/posologyr/articles/a_posteriori_dosing.html","id":"optimal-dose-selection-a-posteriori","dir":"Articles","previous_headings":"A posteriori dose selection","what":"Optimal dose selection a posteriori","title":"A posteriori dose selection","text":"optimal dose achieve peak concentration 80 mg/l can determined using MAP estimates. next dose 2450 mg.","code":"map_dose <- poso_dose_conc(dat=df_patientA,                            prior_model=mod_amikacin_Burdet2015,                            tdm=TRUE,                            time_c = 35,               #target concentration at t = 35 h                            time_dose = 34,            #dosing at t = 34 h                            duration = 0.5,                            target_conc = 80) map_dose #> $dose #> [1] 2447.917 #>  #> $type_of_estimate #> [1] \"point estimate\" #>  #> $conc_estimate #> [1] 80 #>  #> $indiv_param #>   THETA_Cl THETA_Vc THETA_Vp THETA_Q add_sd prop_sd    ETA_Cl    ETA_Vc #> 3      4.3     15.9     21.4    12.1    0.2     0.1 0.4499608 0.2730596 #>      ETA_Vp      ETA_Q CLCREAT4H TBW PoverF #> 3 0.7061496 -0.1388505        50  62    169"},{"path":"https://levenc.github.io/posologyr/articles/a_posteriori_dosing.html","id":"interdose-interval-selection-a-posteriori","dir":"Articles","previous_headings":"A posteriori dose selection","what":"Interdose interval selection a posteriori","title":"A posteriori dose selection","text":"optimal inter-dose interval reach Cmin 2.5 mg/L dosing can determined using MAP estimates. interval doses less 38.6 hours allow adequate elimination amikacin infusion.","code":"map_interval <- poso_inter_cmin(dat=df_patientA,                                 prior_model=mod_amikacin_Burdet2015,                                 dose = map_dose$dose,                                 duration = 0.5,                                 target_cmin = 2.5) map_interval #> $interval #> [1] 38.57781 #>  #> $type_of_estimate #> [1] \"point estimate\" #>  #> $conc_estimate #> [1] 2.500173 #>  #> $indiv_param #>   THETA_Cl THETA_Vc THETA_Vp THETA_Q add_sd prop_sd   ETA_Cl    ETA_Vc #> 1      4.3     15.9     21.4    12.1    0.2     0.1 0.449952 0.2730587 #>      ETA_Vp      ETA_Q CLCREAT4H TBW PoverF #> 1 0.7061588 -0.1388432        50  62    169"},{"path":"https://levenc.github.io/posologyr/articles/a_priori_dosing.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"A priori dose selection","text":"First dose selection critical care patient treated amikacin suspected ventilator-associated pneumonia. Population pharmacokinetic (ppk) model form Burdet et al. 2015.","code":"mod_amikacin_Burdet2015 <- function() {     ini({       THETA_Cl=4.3       THETA_Vc=15.9       THETA_Vp=21.4       THETA_Q=12.1       ETA_Cl + ETA_Vc + ETA_Vp + ETA_Q ~         c(0.1,           0.01     ,   0.05 ,           0.01     ,   0.02 ,   0.2  ,           -0.06    ,   0.004,   0.003,    0.08)       add_sd <- 0.2       prop_sd <- 0.1     })     model({       TVCl  = THETA_Cl*(CLCREAT4H/82)^0.7       TVVc  = THETA_Vc*(TBW/78)^0.9*(PoverF/169)^0.4       TVVp  = THETA_Vp       TVQ   = THETA_Q       Cl    = TVCl*exp(ETA_Cl)       Vc    = TVVc*exp(ETA_Vc)       Vp    = TVVp*exp(ETA_Vp)       Q     = TVQ *exp(ETA_Q)       ke    = Cl/Vc       k12   = Q/Vc       k21   = Q/Vp       Cp    = centr/Vc       d/dt(centr)  = - ke*centr - k12*centr + k21*periph       d/dt(periph) =            + k12*centr - k21*periph        Cp ~ add(add_sd) + prop(prop_sd) + combined1()     })   }"},{"path":[]},{"path":"https://levenc.github.io/posologyr/articles/a_priori_dosing.html","id":"patient-record","dir":"Articles","previous_headings":"A priori dose selection","what":"Patient record","title":"A priori dose selection","text":"first administration, concentration information available. patient record contains information required fill covariates model: CLCREAT4H: 4-h creatinine clearance ml/min TBW: Total body weight kg PoverF: PaO2/FIO2 ratio mmHg","code":"df_patientA <- data.frame(ID=1,TIME=0,                                 DV=0,                                 EVID=0,                                 AMT=0,                                 CLCREAT4H=50,TBW=62,PoverF=169) df_patientA #>   ID TIME DV EVID AMT CLCREAT4H TBW PoverF #> 1  1    0  0    0   0        50  62    169"},{"path":"https://levenc.github.io/posologyr/articles/a_priori_dosing.html","id":"optimal-dose-selection","dir":"Articles","previous_headings":"A priori dose selection","what":"Optimal dose selection","title":"A priori dose selection","text":"absence measured concentrations, optimal dose mg achieve concentration 80 mg/l one hour start 30-minute infusion determined typical profile ppk model.","code":"prior_dose <- poso_dose_conc(dat=df_patientA,                              prior_model=mod_amikacin_Burdet2015,                              time_c = 1,                        #30 min after a                                duration = 0.5,                    #30 min infusion                              target_conc = 80) prior_dose #> $dose #> [1] 2087.669 #>  #> $type_of_estimate #> [1] \"point estimate\" #>  #> $conc_estimate #> [1] 80 #>  #> $indiv_param #>   THETA_Cl THETA_Vc THETA_Vp THETA_Q add_sd prop_sd       ETA_Cl       ETA_Vc #> 1      4.3     15.9     21.4    12.1    0.2     0.1 2.025724e-07 6.573817e-08 #>         ETA_Vp         ETA_Q CLCREAT4H TBW PoverF #> 1 2.011353e-07 -1.163665e-07        50  62    169"},{"path":"https://levenc.github.io/posologyr/articles/a_priori_dosing.html","id":"time-required-to-reach-the-target-cmin","dir":"Articles","previous_headings":"A priori dose selection","what":"Time required to reach the target Cmin","title":"A priori dose selection","text":"Following dose, time hours required reach target Cmin concentration 2.5 mg/l can estimated.","code":"poso_time_cmin(dat=df_patientA,                prior_model=mod_amikacin_Burdet2015,                dose = prior_dose$dose,                duration = 0.5,                                  #30 min infusion                target_cmin = 2.5) #> $time #> [1] 37.5 #>  #> $type_of_estimate #> [1] \"point estimate\" #>  #> $cmin_estimate #> [1] 2.49637 #>  #> $indiv_param #>   THETA_Cl THETA_Vc THETA_Vp THETA_Q add_sd prop_sd        ETA_Cl       ETA_Vc #> 1      4.3     15.9     21.4    12.1    0.2     0.1 -9.803026e-07 3.556777e-07 #>          ETA_Vp        ETA_Q CLCREAT4H TBW PoverF #> 1 -3.567767e-07 9.847164e-07        50  62    169"},{"path":"https://levenc.github.io/posologyr/articles/a_priori_dosing.html","id":"plotting-the-selected-dosage","dir":"Articles","previous_headings":"A priori dose selection","what":"Plotting the selected dosage","title":"A priori dose selection","text":"selected dose can simulated plotted. setting n_simul = 0, poso_simu_pop() function produces compiled rxode2 model without inter-individual variability, using typical population parameter values individual covariates patient record. Observations 30-minutes infusion optimal dose added rxode2 model updating rxode2 event table. Plotting simulated scenario.  resulting plot can augmented ggplot2. example, adding horizontal ribbon showing 60-80 mg/l target interval 1 h peak concentration, vertical dashed line marking 1 hour.  typical patient (.e. PK profile typical model population), selected dose meets peak concentration target.","code":"# generate a model using the individual covariates  simu_patA      <- poso_simu_pop(dat=df_patientA,                                 prior_model=mod_amikacin_Burdet2015,                                 n_simul = 0) simu_patA$model$time <- seq(0,20,b=0.1) simu_patA$model$add.dosing(dose=prior_dose$dose,rate=prior_dose$dose/0.5) plot(simu_patA$model,Cc) plot(simu_patA$model,Cc) +    ggplot2::ylab(\"Central concentration\") +   ggplot2::geom_vline(xintercept=1, linetype=\"dashed\") +   ggplot2::geom_ribbon(ggplot2::aes(ymin=60, ymax=80),                        fill=\"seagreen\",show.legend = FALSE, alpha=0.15)"},{"path":"https://levenc.github.io/posologyr/articles/auc_based_dosing.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"AUC-based dose selection","text":"AUC-based dosage adjustment patient treated vancomycin methicillin-resistant Staphylococcus aureus blood stream infection, using population pharmacokinetic (ppk) model Goti et al. 2018, using data therapeutic drug monitoring (TDM).","code":"mod_vancomycin_Goti2018 <- function() {     ini({       THETA_Cl <- 4.5       THETA_Vc <- 58.4       THETA_Vp <- 38.4       THETA_Q <- 6.5       ETA_Cl ~ 0.147       ETA_Vc ~ 0.510       ETA_Vp ~ 0.282       add.sd <- 3.4       prop.sd <- 0.227     })     model({       TVCl  = THETA_Cl*(CLCREAT/120)^0.8*(0.7^DIAL);       TVVc  = THETA_Vc*(WT/70)          *(0.5^DIAL);       TVVp  = THETA_Vp;       TVQ   = THETA_Q;       Cl    = TVCl*exp(ETA_Cl);       Vc    = TVVc*exp(ETA_Vc);       Vp    = TVVp*exp(ETA_Vp);       Q     = TVQ;       ke    = Cl/Vc;       k12   = Q/Vc;       k21   = Q/Vp;       Cc    = centr/Vc;       d/dt(centr)  = - ke*centr - k12*centr + k21*periph;       d/dt(periph) =            + k12*centr - k21*periph;       d/dt(AUC) <- Cc       Cc ~ add(add.sd) + prop(prop.sd) + combined1()     })   }"},{"path":[]},{"path":"https://levenc.github.io/posologyr/articles/auc_based_dosing.html","id":"patient-record-with-tdm-data","dir":"Articles","previous_headings":"Discontinuous intravenous infusion","what":"Patient record with TDM data","title":"AUC-based dose selection","text":"dosage selection can informed using results TDM. See vignette(\"patient_data_input\") details regarding patient records.","code":"df_patientB <- data.frame(ID=1,TIME=c(0.0,13.0,24.2,48),                           DV=c(NA,12,NA,9.5),                           AMT=c(2000,0,1000,0),                           DUR=c(2,NA,2,NA),                           EVID=c(1,0,1,0),                           CLCREAT=65,WT=70,DIAL=0) df_patientB #>   ID TIME   DV  AMT DUR EVID CLCREAT WT DIAL #> 1  1  0.0   NA 2000   2    1      65 70    0 #> 2  1 13.0 12.0    0  NA    0      65 70    0 #> 3  1 24.2   NA 1000   2    1      65 70    0 #> 4  1 48.0  9.5    0  NA    0      65 70    0"},{"path":"https://levenc.github.io/posologyr/articles/auc_based_dosing.html","id":"estimate-the-map-individual-parameters","dir":"Articles","previous_headings":"Discontinuous intravenous infusion","what":"Estimate the MAP individual parameters","title":"AUC-based dose selection","text":"","code":"patB_map <- poso_estim_map(dat=df_patientB,                            prior_model=mod_vancomycin_Goti2018)"},{"path":"https://levenc.github.io/posologyr/articles/auc_based_dosing.html","id":"plot-the-individual-pharmacokinetic-profile","dir":"Articles","previous_headings":"Discontinuous intravenous infusion","what":"Plot the individual pharmacokinetic profile","title":"AUC-based dose selection","text":"individual pharmacokinetic profile can plotted using rxode2 model provided poso_estim_map() function.  Using ggplot2 observed data points can added plot  MAP profile matches observations.","code":"plot(patB_map$model,Cc) #Get the observations from the patient record indiv_obs             <- df_patientB[,c(\"DV\",\"TIME\")] names(indiv_obs)      <- c(\"value\",\"time\")  #Overlay the MAP profile and the observations plot(patB_map$model,Cc) +   ggplot2::ylab(\"Central concentration\") +   ggplot2::geom_point(data=indiv_obs, size= 3, na.rm=TRUE)"},{"path":"https://levenc.github.io/posologyr/articles/auc_based_dosing.html","id":"get-the-auc24-from-the-map-model","dir":"Articles","previous_headings":"Discontinuous intravenous infusion","what":"Get the AUC24 from the MAP model","title":"AUC-based dose selection","text":"Considering MIC 1 mg/L, target AUC 24 hours (AUC24) 400 mg.h/L. AUC can retrieved rxode2 model using usual R data.frame syntax. current dosage meet target AUC.","code":"#AUC 0_24 AUC_map_first_dose <- patB_map$model$AUC[which(patB_map$model$time == 24)] AUC_map_first_dose #> [1] 337.8965  #AUC 24_48 AUC_map_second_dose <- patB_map$model$AUC[which(patB_map$model$time == 48)] - AUC_map_first_dose AUC_map_second_dose #> [1] 325.3072"},{"path":"https://levenc.github.io/posologyr/articles/auc_based_dosing.html","id":"optimal-dose-selection-a-posteriori","dir":"Articles","previous_headings":"Discontinuous intravenous infusion","what":"Optimal dose selection a posteriori","title":"AUC-based dose selection","text":"next dose needed achieve AUC24 400 mg.h/L can estimated using TDM data. optimal dose estimated next infusion 1411 mg.","code":"poso_dose_auc(dat=df_patientB,               prior_model=mod_vancomycin_Goti2018,               tdm=TRUE,               time_auc=24,            #AUC24               time_dose = 48,         #48 h: immediately following the last observation               duration=2,             #infused over 2 h               target_auc=400) #> $dose #> [1] 1411.593 #>  #> $type_of_estimate #> [1] \"point estimate\" #>  #> $auc_estimate #> [1] 400 #>  #> $indiv_param #>   THETA_Cl THETA_Vc THETA_Vp THETA_Q add.sd prop.sd     ETA_Cl     ETA_Vc #> 4      4.5     58.4     38.4     6.5    3.4   0.227 0.08208203 0.06122374 #>       ETA_Vp CLCREAT DIAL WT #> 4 0.06285943      65    0 70"},{"path":"https://levenc.github.io/posologyr/articles/auc_based_dosing.html","id":"optimal-maintenance-dose-selection-a-posteriori","dir":"Articles","previous_headings":"Discontinuous intravenous infusion","what":"Optimal maintenance dose selection a posteriori","title":"AUC-based dose selection","text":"maintenance dose needed reliably achieve AUC24 400 mg.h/L can estimated simulating multiple dose regimen enough administrations (e.g. 11 consecutive administrations, add_dose=10) approximate steady-state. optimal maintenance dose 1200 mg.","code":"poso_dose_auc(dat=df_patientB,               prior_model=mod_vancomycin_Goti2018,               time_auc=24,               starting_time=24*9,               interdose_interval=24,               add_dose=10,               duration=2,               target_auc=400) #> $dose #> [1] 1198.266 #>  #> $type_of_estimate #> [1] \"point estimate\" #>  #> $auc_estimate #> [1] 400 #>  #> $indiv_param #>   THETA_Cl THETA_Vc THETA_Vp THETA_Q add.sd prop.sd     ETA_Cl     ETA_Vc #> 1      4.5     58.4     38.4     6.5    3.4   0.227 0.08208199 0.06122337 #>      ETA_Vp CLCREAT DIAL WT #> 1 0.0628591      65    0 70"},{"path":"https://levenc.github.io/posologyr/articles/auc_based_dosing.html","id":"continuous-intravenous-infusion","dir":"Articles","previous_headings":"","what":"Continuous intravenous infusion","title":"AUC-based dose selection","text":"maintenance dose continuous intravenous infusion can easily determined setting duration infusion equal interdose_interval. optimal maintenance dose also 1200 mg / 24 h continuous intravenous infusion.","code":"poso_dose_auc(dat=df_patientB,               prior_model=mod_vancomycin_Goti2018,               time_auc=24,               starting_time=24*9,               interdose_interval=24,               add_dose=10,               duration=24,               target_auc=400) #> $dose #> [1] 1198.923 #>  #> $type_of_estimate #> [1] \"point estimate\" #>  #> $auc_estimate #> [1] 400 #>  #> $indiv_param #>   THETA_Cl THETA_Vc THETA_Vp THETA_Q add.sd prop.sd     ETA_Cl     ETA_Vc #> 1      4.5     58.4     38.4     6.5    3.4   0.227 0.08208202 0.06122453 #>       ETA_Vp CLCREAT DIAL WT #> 1 0.06285936      65    0 70"},{"path":"https://levenc.github.io/posologyr/articles/classic_posologyr_models.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Classic posologyr models","text":"Originally, posologyr models R lists; posologyr still uses format internally, ’s often useful use rxode2 syntax, even import NONMEM model using nonmem2rx package. article describes structure classical posologyr models. illustrates define published population models.","code":""},{"path":"https://levenc.github.io/posologyr/articles/classic_posologyr_models.html","id":"structure","dir":"Articles","previous_headings":"","what":"Structure","title":"Classic posologyr models","text":"posologyr model named R list following items: ppk_model rxode2 model implementing structural population model individual model (.e. model inter-individual variability) covariates error_model function residual error model, alternatively named list functions multiple endpoints model vignette(\"multiple_endpoints\") theta named vector population estimates fixed effects parameters (called THETAs, following NONMEM terminology) omega named square variance-covariance matrix population parameters inter-individual variability sigma estimates parameters residual error model pi_matrix Optional. named square variance-covariance matrix population parameters inter-occasion variability covariates character vector covariates model","code":""},{"path":"https://levenc.github.io/posologyr/articles/classic_posologyr_models.html","id":"definition-of-a-prior-model-through-an-example","dir":"Articles","previous_headings":"","what":"Definition of a prior model through an example","title":"Classic posologyr models","text":"model example two-compartment ppk model vancomycin derived retrospective study cohort 1,800 patients (doi:10.1097/FTD.0000000000000490).","code":""},{"path":"https://levenc.github.io/posologyr/articles/classic_posologyr_models.html","id":"ppk_model","dir":"Articles","previous_headings":"Definition of a prior model through an example","what":"ppk_model","title":"Classic posologyr models","text":"model defined rxode2 mini-language. posologyr needs structural model, defined either differential algebraic equations, individual model. Depending model type, naming conventions less strict: Single endpoint model (e.g. pharmacokinetic models) concentration central compartment must named Cc. Multiple endpoints model (eg. PK-PD, parent-metabolite, blood-urine…) names endpoints flexible, must consistent names error models parameters. differential function d/dt(AUC) =  Cc; needed optimization function poso_dose_auc().","code":"ppk_model   = rxode2::rxode({     centr(0) = 0;     TVCl  = THETA_Cl*(CLCREAT/120)^0.8*(0.7^DIAL);     TVVc  = THETA_Vc*(WT/70)          *(0.5^DIAL);     TVVp  = THETA_Vp;     TVQ   = THETA_Q;     Cl    = TVCl*exp(ETA_Cl);     Vc    = TVVc*exp(ETA_Vc);     Vp    = TVVp*exp(ETA_Vp);     Q     = TVQ;     ke    = Cl/Vc;     k12   = Q/Vc;     k21   = Q/Vp;     Cc    = centr/Vc;     d/dt(centr)  = - ke*centr - k12*centr + k21*periph;     d/dt(periph) =            + k12*centr - k21*periph;     d/dt(AUC)    =   Cc;   })"},{"path":"https://levenc.github.io/posologyr/articles/classic_posologyr_models.html","id":"error_model","dir":"Articles","previous_headings":"Definition of a prior model through an example","what":"error_model","title":"Classic posologyr models","text":"function residual error model, taking two arguments: simulated concentrations, vector sigma estimates parameters residual error model. multiple endpoints models, error_model must named list function endpoint vignette(\"multiple_endpoints\"). obtain individual estimations multiple endpoints, consistency naming convention must maintained across following: dataset (using column DVID). residual error models (stored named list). standard deviation residual error models (stored named list, see sigma). many residual error models desired can defined. model defined named list error_models must counterpart named list sigma, names must match defined DVID column dataset.","code":"error_model <- function(f,sigma){           #additive model if sigma[2] == 0   g <- sigma[1]^2 + (sigma[2]^2)*(f^2)      #proportional model if sigma[1] == 0   return(sqrt(g)) } error_model = list(     first_endpoint = function(f,sigma){       g <- sigma[1]^2 + (sigma[2]^2)*(f^2)       return(sqrt(g))     },     second_endpoint = function(f,sigma){       g <- sigma[1]^2 + (sigma[2]^2)*(f^2)       return(sqrt(g))     }   )"},{"path":"https://levenc.github.io/posologyr/articles/classic_posologyr_models.html","id":"theta","dir":"Articles","previous_headings":"Definition of a prior model through an example","what":"theta","title":"Classic posologyr models","text":"estimations parameters fixed effects model (THETA), named vector. names must match names used ppk_model.","code":"theta = c(THETA_Cl=4.5, THETA_Vc=58.4, THETA_Vp=38.4, THETA_Q=6.5)"},{"path":"https://levenc.github.io/posologyr/articles/classic_posologyr_models.html","id":"omega","dir":"Articles","previous_headings":"Definition of a prior model through an example","what":"omega","title":"Classic posologyr models","text":"variance-covariance matrix random effects (ETA) individual model. symmetric matrix. names must match names used ppk_model. easy way define using lotri::lotri(). estimates variances random effects can given different parameterizations depending authors. Standard deviation (SD): square root variance, returned Monolix Coefficient variation (CV): calculated sqrt(exp(SD^2)-1), variance can computed back log((CV^2)+1) Full covariance matrix: easiest reuse, less common literature case vancomycin model, estimates subject variability (BSV) given CV%. must converted variances prior inclusion omega. estimates covariance (-diagonal) sometimes given coefficients correlation ETAs. covariance ETA_a ETA_b can computed following product: standard_deviation(ETA_a) * standard_deviation(ETA_b) * correlation(ETA_a ETA_b). example, covariances equal zero.","code":"omega = lotri::lotri({ETA_Cl + ETA_Vc + ETA_Vp + ETA_Q ~                           c(0.147,                             0    ,  0.510 ,                             0    ,  0     ,   0.282,                             0    ,  0     ,   0    ,    0)})"},{"path":"https://levenc.github.io/posologyr/articles/classic_posologyr_models.html","id":"sigma","dir":"Articles","previous_headings":"Definition of a prior model through an example","what":"sigma","title":"Classic posologyr models","text":"estimates parameters residual error model standard deviation scale, either vector: matrix: named list (multiple endpoints): depending residual error model.","code":"sigma       = c(additive_a = 3.4, proportional_b = 0.227) sigma       = lotri::lotri({prop + add ~ c(0.227,0.0,3.4)}) sigma       = list(     first_endpoint=c(additive_a = 0.144, proportional_b = 0.15),     second_endpoint=c(additive_a = 3.91, proportional_b = 0.0)     )"},{"path":"https://levenc.github.io/posologyr/articles/classic_posologyr_models.html","id":"pi_matrix","dir":"Articles","previous_headings":"Definition of a prior model through an example","what":"pi_matrix","title":"Classic posologyr models","text":"Optional: needed models inter-occasion variability (IOV). variance-covariance matrix random effects (KAPPA) IOV. omega matrix, names must match names used ppk_model. easy way define using lotri::lotri().","code":"pi_matrix = lotri::lotri({KAPPA_Cl + KAPPA_Vc ~       c(0.1934626,         0.00     ,  0.05783106)})"},{"path":"https://levenc.github.io/posologyr/articles/classic_posologyr_models.html","id":"covariates","dir":"Articles","previous_headings":"Definition of a prior model through an example","what":"covariates","title":"Classic posologyr models","text":"names every covariate defined ppk_model, character vector.","code":"covariates  = c(\"CLCREAT\",\"WT\",\"DIAL\")"},{"path":"https://levenc.github.io/posologyr/articles/classic_posologyr_models.html","id":"full-model","dir":"Articles","previous_headings":"Definition of a prior model through an example","what":"Full model","title":"Classic posologyr models","text":"posologyr model list objects. Note: model include inter-occasion variability, pi_matrix omitted.","code":"mod_vancomyin_Goti2018 <- list(   ppk_model   = rxode2::rxode({      centr(0) = 0;     TVCl  = THETA_Cl*(CLCREAT/120)^0.8*(0.7^DIAL);     TVVc  = THETA_Vc*(WT/70)          *(0.5^DIAL);     TVVp  = THETA_Vp;     TVQ   = THETA_Q;     Cl    = TVCl*exp(ETA_Cl);     Vc    = TVVc*exp(ETA_Vc);     Vp    = TVVp*exp(ETA_Vp);     Q     = TVQ;     ke    = Cl/Vc;     k12   = Q/Vc;     k21   = Q/Vp;     Cc    = centr/Vc;     d/dt(centr)  = - ke*centr - k12*centr + k21*periph;     d/dt(periph) =            + k12*centr - k21*periph;     d/dt(AUC)    =   Cc;   }),   error_model = function(f,sigma){     g <- sigma[1] + sigma[2]*f     return(g)   },   theta = c(THETA_Cl=4.5, THETA_Vc=58.4, THETA_Vp=38.4,THETA_Q=6.5),   omega = lotri::lotri({ETA_Cl + ETA_Vc + ETA_Vp + ETA_Q ~                           c(0.147,                             0    ,  0.510 ,                             0    ,  0     ,   0.282,                             0    ,  0     ,   0    ,    0)}),   sigma       = c(additive_a = 3.4, proportional_b = 0.227),   covariates  = c(\"CLCREAT\",\"WT\",\"DIAL\"))"},{"path":"https://levenc.github.io/posologyr/articles/classic_posologyr_models.html","id":"resulting-r-object","dir":"Articles","previous_headings":"Definition of a prior model through an example > Full model","what":"Resulting R object","title":"Classic posologyr models","text":"","code":"mod_vancomyin_Goti2018 #> $ppk_model #> rxode2 NA model named rx_bce7176cfa18100af62e71ae38d3cd48 model (✔ ready).  #> $state: centr, periph, AUC #> $params: THETA_Cl, CLCREAT, DIAL, THETA_Vc, WT, THETA_Vp, THETA_Q, ETA_Cl, ETA_Vc, ETA_Vp #> $lhs: TVCl, TVVc, TVVp, TVQ, Cl, Vc, Vp, Q, ke, k12, k21, Cc #>  #> $error_model #> function(f,sigma){ #>     g <- sigma[1] + sigma[2]*f #>     return(g) #>   } #>  #> $theta #> THETA_Cl THETA_Vc THETA_Vp  THETA_Q  #>      4.5     58.4     38.4      6.5  #>  #> $omega #>        ETA_Cl ETA_Vc ETA_Vp ETA_Q #> ETA_Cl  0.147   0.00  0.000     0 #> ETA_Vc  0.000   0.51  0.000     0 #> ETA_Vp  0.000   0.00  0.282     0 #> ETA_Q   0.000   0.00  0.000     0 #>  #> $sigma #>     additive_a proportional_b  #>          3.400          0.227  #>  #> $covariates #> [1] \"CLCREAT\" \"WT\"      \"DIAL\""},{"path":"https://levenc.github.io/posologyr/articles/multiple_endpoints.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Multiple endpoints","text":"different error model can defined multiple endpoints models (eg. PK-PD, parent-metabolite, blood-urine…). example can seen , utilizing warfarin data model (provided Tomoo Funaki Nick Holford) nlmixr documentation (https://nlmixr2.org/articles/multiple-endpoints.html).","code":""},{"path":"https://levenc.github.io/posologyr/articles/multiple_endpoints.html","id":"warfarin-pkpd-model","dir":"Articles","previous_headings":"Introduction","what":"warfarin PKPD model","title":"Multiple endpoints","text":"","code":"mod_warfarin_nlmixr <- function() {     ini({       #Fixed effects: population estimates        THETA_ktr=0.106       THETA_ka=-0.087       THETA_cl=-2.03       THETA_v=2.07       THETA_emax=3.4       THETA_ec50=0.00724       THETA_kout=-2.9       THETA_e0=4.57              #Random effects: inter-individual variability       ETA_ktr ~ 1.024695       ETA_ka ~ 0.9518403       ETA_cl ~ 0.5300943       ETA_v ~ 0.4785394       ETA_emax ~ 0.7134424       ETA_ec50 ~ 0.7204165       ETA_kout ~ 0.3563706       ETA_e0 ~ 0.2660827              #Unexplained residual variability       cp.sd <- 0.144       cp.prop.sd <- 0.15       pca.sd <- 3.91     })     model({       #Individual model and covariates       ktr <- exp(THETA_ktr + ETA_ktr)       ka <- exp(THETA_ka + ETA_ka)       cl <- exp(THETA_cl + ETA_cl)       v <- exp(THETA_v + ETA_v)       emax = expit(THETA_emax + ETA_emax)       ec50 =  exp(THETA_ec50 + ETA_ec50)       kout = exp(THETA_kout + ETA_kout)       e0 = exp(THETA_e0 + ETA_e0)              #Structural model defined using ordinary differential equations (ODE)       DCP = center/v       PD=1-emax*DCP/(ec50+DCP)              effect(0) = e0       kin = e0*kout              d/dt(depot) = -ktr * depot       d/dt(gut) =  ktr * depot -ka * gut       d/dt(center) =  ka * gut - cl / v * center       d/dt(effect) = kin*PD -kout*effect              cp = center / v       pca = effect              #Model for unexplained residual variability       cp ~ add(cp.sd) + prop(cp.prop.sd)       pca ~ add(pca.sd)     })   }"},{"path":"https://levenc.github.io/posologyr/articles/multiple_endpoints.html","id":"data-first-subject-from-the-warfarin-dataset","dir":"Articles","previous_headings":"Introduction","what":"data: first subject from the warfarin dataset","title":"Multiple endpoints","text":"posologyr can compute EBE combined PKPD model poso_estim_map() observation/time curves endpoints can also plotted","code":"warf_01 <- data.frame(ID=1,                       TIME=c(0.0,1.0,3.0,6.0,24.0,24.0,36.0,36.0,48.0,48.0,72.0,72.0,144.0),                       DV=c(0.0,1.9,6.6,10.8,5.6,44.0,4.0,27.0,2.7,28.0,0.8,31.0,71.0),                       DVID=c(\"cp\",\"cp\",\"cp\",\"cp\",\"cp\",\"pca\",\"cp\",\"pca\",\"cp\",\"pca\",\"cp\",\"pca\",\"pca\"),                       EVID=c(1,0,0,0,0,0,0,0,0,0,0,0,0),                       AMT=c(100,0,0,0,0,0,0,0,0,0,0,0,0)) warf_01 #>    ID TIME   DV DVID EVID AMT #> 1   1    0  0.0   cp    1 100 #> 2   1    1  1.9   cp    0   0 #> 3   1    3  6.6   cp    0   0 #> 4   1    6 10.8   cp    0   0 #> 5   1   24  5.6   cp    0   0 #> 6   1   24 44.0  pca    0   0 #> 7   1   36  4.0   cp    0   0 #> 8   1   36 27.0  pca    0   0 #> 9   1   48  2.7   cp    0   0 #> 10  1   48 28.0  pca    0   0 #> 11  1   72  0.8   cp    0   0 #> 12  1   72 31.0  pca    0   0 #> 13  1  144 71.0  pca    0   0 map_warf_01  <- poso_estim_map(warf_01,mod_warfarin_nlmixr) map_warf_01 #> $eta #>     ETA_ktr      ETA_ka      ETA_cl       ETA_v    ETA_emax    ETA_ec50  #> -0.32052193 -0.54227235  0.79432182 -0.02986447  0.02383273 -0.28133882  #>    ETA_kout      ETA_e0  #> -0.30872226 -0.08386652  #>  #> $model #> ── Solved rxode2 object ── #> ── Parameters ($params): ── #>   THETA_ktr    THETA_ka    THETA_cl     THETA_v  THETA_emax  THETA_ec50  #>  0.10600000 -0.08700000 -2.03000000  2.07000000  3.40000000  0.00724000  #>  THETA_kout    THETA_e0       cp.sd  cp.prop.sd      pca.sd     ETA_ktr  #> -2.90000000  4.57000000  0.14400000  0.15000000  3.91000000 -0.32052193  #>      ETA_ka      ETA_cl       ETA_v    ETA_emax    ETA_ec50    ETA_kout  #> -0.54227235  0.79432182 -0.02986447  0.02383273 -0.28133882 -0.30872226  #>      ETA_e0  #> -0.08386652  #> ── Initial Conditions ($inits): ── #>  depot    gut center effect  #>      0      0      0      0  #> ── First part of data (object): ── #> # A tibble: 1,451 × 18 #>    time   ktr    ka    cl     v  emax  ec50   kout    e0    DCP    PD   kin #>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl> <dbl>  <dbl> <dbl> <dbl> #> 1   0   0.807 0.533 0.291  7.69 0.968 0.760 0.0404  88.8 0      1      3.59 #> 2   0.1 0.807 0.533 0.291  7.69 0.968 0.760 0.0404  88.8 0.0267 0.967  3.59 #> 3   0.2 0.807 0.533 0.291  7.69 0.968 0.760 0.0404  88.8 0.102  0.885  3.59 #> 4   0.3 0.807 0.533 0.291  7.69 0.968 0.760 0.0404  88.8 0.219  0.783  3.59 #> 5   0.4 0.807 0.533 0.291  7.69 0.968 0.760 0.0404  88.8 0.373  0.681  3.59 #> 6   0.5 0.807 0.533 0.291  7.69 0.968 0.760 0.0404  88.8 0.557  0.590  3.59 #> # ℹ 1,445 more rows #> # ℹ 6 more variables: cp <dbl>, pca <dbl>, depot <dbl>, gut <dbl>, #> #   center <dbl>, effect <dbl> #>  #> $event #>          id  time   amt  evid #>       <int> <num> <num> <int> #>    1:     1   0.0    NA     0 #>    2:     1   0.0   100     1 #>    3:     1   0.1    NA     0 #>    4:     1   0.2    NA     0 #>    5:     1   0.3    NA     0 #>   ---                         #> 1448:     1 144.6    NA     0 #> 1449:     1 144.7    NA     0 #> 1450:     1 144.8    NA     0 #> 1451:     1 144.9    NA     0 #> 1452:     1 145.0    NA     0 plot(map_warf_01$model,\"cp\") plot(map_warf_01$model,\"pca\")"},{"path":"https://levenc.github.io/posologyr/articles/patient_data.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Patient data","text":"describes structure patient records compatible posologyr.","code":""},{"path":"https://levenc.github.io/posologyr/articles/patient_data.html","id":"structure","dir":"Articles","previous_headings":"","what":"Structure","title":"Patient data","text":"Data input posologyr type data input rxode2. stated rxode2 documentation (rxode2 datasets), also similar data NONMEM. patient dataset table sequential event records, line event. description different event types available rxode2 documentation (rxode2 Event Types). minimal working example:","code":"data.frame(ID=1,            TIME=c(0.0,3),            DV=c(NA,60.0),            AMT=c(1000,0),            EVID=c(101,0)) #>   ID TIME DV  AMT EVID #> 1  1    0 NA 1000  101 #> 2  1    3 60    0    0"},{"path":"https://levenc.github.io/posologyr/articles/patient_data.html","id":"required-fields-and-data","dir":"Articles","previous_headings":"Structure","what":"Required fields and data","title":"Patient data","text":"TIME Dosing times, sampling times therapeutic drug monitoring (TDM). units depend specification population pharmacokinetics (ppk) model. AMT Amount drug administered. units depend specification ppk model. EVID Event type. Must 0 observations (concentrations TDM). DV concentrations. Must NA EVID 0. Covariates Every covariate defined prior posologyr model. column names must match covariate vector ppk model. OCC Occasions. Required models inter-occasion variability. DVID Observation type. Required models multiple endpoints. CMT Name (number) compartment dose administered, see vignette(\"route_of_administration\").","code":""},{"path":[]},{"path":"https://levenc.github.io/posologyr/articles/patient_data.html","id":"a-priori-dose-adjustment","dir":"Articles","previous_headings":"Common use cases","what":"A priori dose adjustment","title":"Patient data","text":"dosing: simplest patient record single line dataframe, individual patient covariates, columns (TIME, DV, AMT, EVID) set zero.","code":"data.frame(ID=1,            TIME=0,            DV=0,            AMT=0,            EVID=0,            COVAR1=c(\"X\"),            COVAR2=c(\"Y\")) #>   ID TIME DV AMT EVID COVAR1 COVAR2 #> 1  1    0  0   0    0      X      Y"},{"path":"https://levenc.github.io/posologyr/articles/patient_data.html","id":"oral-administration-or-iv-bolus","dir":"Articles","previous_headings":"Common use cases","what":"Oral administration or IV bolus","title":"Patient data","text":"EVID = 1 instantaneous administration first compartment defined ppk model: either central compartment IV bolus, depot compartment oral administration.","code":"data.frame(ID=1,            TIME=c(0.0,3),            DV=c(NA,60.0),            AMT=c(1000,0),            EVID=c(1,0),            COVAR1=c(\"X\"),            COVAR2=c(\"Y\")) #>   ID TIME DV  AMT EVID COVAR1 COVAR2 #> 1  1    0 NA 1000    1      X      Y #> 2  1    3 60    0    0      X      Y"},{"path":"https://levenc.github.io/posologyr/articles/patient_data.html","id":"intermittent-infusion","dir":"Articles","previous_headings":"Common use cases","what":"Intermittent infusion","title":"Patient data","text":"EVID = 1 bolus infusion, administered first compartment defined ppk model. DUR defines duration. AMT amount administered duration DUR.","code":"data.frame(ID=1,            TIME=c(0.0,1.0,14.0),            DV=c(NA,25.0,5.5),            AMT=c(1000,0,0),            DUR=c(0.5,NA,NA),            EVID=c(1,0,0),            COVAR1=c(\"X\"),            COVAR2=c(\"Y\")) #>   ID TIME   DV  AMT DUR EVID COVAR1 COVAR2 #> 1  1    0   NA 1000 0.5    1      X      Y #> 2  1    1 25.0    0  NA    0      X      Y #> 3  1   14  5.5    0  NA    0      X      Y"},{"path":"https://levenc.github.io/posologyr/articles/patient_data.html","id":"inter-occasion-variability","dir":"Articles","previous_headings":"Common use cases","what":"Inter-occasion variability","title":"Patient data","text":"given occasion, value OCC must repeated row. OCC must specified rows table.","code":"data.frame(ID=1,            TIME=c(0.0,1.0,14.0,24.0,25.0,36.0),            DV=c(NA,25.0,5.5,NA,30.0,6.0),            AMT=c(1000,0,0,1000,0,0),            DUR=c(0.5,NA,NA,0.5,NA,NA),            EVID=c(1,0,0,1,0,0),            OCC=c(1,1,1,2,2,2),            COVAR1=c(\"X\"),            COVAR2=c(\"Y\")) #>   ID TIME   DV  AMT DUR EVID OCC COVAR1 COVAR2 #> 1  1    0   NA 1000 0.5    1   1      X      Y #> 2  1    1 25.0    0  NA    0   1      X      Y #> 3  1   14  5.5    0  NA    0   1      X      Y #> 4  1   24   NA 1000 0.5    1   2      X      Y #> 5  1   25 30.0    0  NA    0   2      X      Y #> 6  1   36  6.0    0  NA    0   2      X      Y"},{"path":"https://levenc.github.io/posologyr/articles/patient_data.html","id":"multiple-endpoints","dir":"Articles","previous_headings":"Common use cases","what":"Multiple endpoints","title":"Patient data","text":"DVID used specify type observation. values DVID dataset must match names residual error models, see vignette(\"multiple_endpoints\").","code":"data.frame(ID=1,            TIME=c(0.0,1.0,14.0,24.0,25.0,36.0),            DV=c(NA,20.0,80,35.5,60.0,40.0),            AMT=c(1000,0,0,0,0,0),            EVID=c(1,0,0,0,0,0),            DVID=c(\"parent\",\"parent\",\"metabolite\",\"parent\",\"metabolite\",\"metabolite\"),            COVAR1=c(\"X\"),            COVAR2=c(\"Y\")) #>   ID TIME   DV  AMT EVID       DVID COVAR1 COVAR2 #> 1  1    0   NA 1000    1     parent      X      Y #> 2  1    1 20.0    0    0     parent      X      Y #> 3  1   14 80.0    0    0 metabolite      X      Y #> 4  1   24 35.5    0    0     parent      X      Y #> 5  1   25 60.0    0    0 metabolite      X      Y #> 6  1   36 40.0    0    0 metabolite      X      Y"},{"path":"https://levenc.github.io/posologyr/articles/patient_data_input.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Patient records","text":"describes structure patient records usable posologyr.","code":""},{"path":"https://levenc.github.io/posologyr/articles/patient_data_input.html","id":"structure","dir":"Articles","previous_headings":"","what":"Structure","title":"Patient records","text":"Data input posologyr type data input rxode2. stated rxode2 documentation (rxode2 datasets), also similar data NONMEM. patient dataset table sequential event records, line event. description different event types available rxode2 documentation (rxode2 Event Types). minimal working example:","code":"data.frame(ID=1,            TIME=c(0.0,3),            DV=c(NA,60.0),            AMT=c(1000,0),            EVID=c(101,0)) #>   ID TIME DV  AMT EVID #> 1  1    0 NA 1000  101 #> 2  1    3 60    0    0"},{"path":"https://levenc.github.io/posologyr/articles/patient_data_input.html","id":"required-fields-and-data","dir":"Articles","previous_headings":"Structure","what":"Required fields and data","title":"Patient records","text":"TIME Dosing times, sampling times therapeutic drug monitoring (TDM). units depend specification population pharmacokinetics (ppk) model. AMT Amount drug administered. units depend specification ppk model. EVID Event type. Must 0 observations (concentrations TDM). DV concentrations. Must NA EVID 0. Covariates Every covariate defined prior posologyr model. column names must match covariate vector ppk model. OCC Occasions. Required models inter-occasion variability. DVID Observation type. Required models multiple endpoints. CMT Name (number) compartment dose administered, see vignette(\"route_of_administration\").","code":""},{"path":[]},{"path":"https://levenc.github.io/posologyr/articles/patient_data_input.html","id":"a-priori-dose-adjustment","dir":"Articles","previous_headings":"Common use cases","what":"A priori dose adjustment","title":"Patient records","text":"dosing: simplest patient record single line dataframe, individual patient covariates, columns (TIME, DV, AMT, EVID) set zero.","code":"data.frame(ID=1,            TIME=0,            DV=0,            AMT=0,            EVID=0,            COVAR1=c(\"X\"),            COVAR2=c(\"Y\")) #>   ID TIME DV AMT EVID COVAR1 COVAR2 #> 1  1    0  0   0    0      X      Y"},{"path":"https://levenc.github.io/posologyr/articles/patient_data_input.html","id":"oral-administration-or-iv-bolus","dir":"Articles","previous_headings":"Common use cases","what":"Oral administration or IV bolus","title":"Patient records","text":"EVID = 1 instantaneous administration first compartment defined ppk model: either central compartment IV bolus, depot compartment oral administration.","code":"data.frame(ID=1,            TIME=c(0.0,3),            DV=c(NA,60.0),            AMT=c(1000,0),            EVID=c(1,0),            COVAR1=c(\"X\"),            COVAR2=c(\"Y\")) #>   ID TIME DV  AMT EVID COVAR1 COVAR2 #> 1  1    0 NA 1000    1      X      Y #> 2  1    3 60    0    0      X      Y"},{"path":"https://levenc.github.io/posologyr/articles/patient_data_input.html","id":"intermittent-infusion","dir":"Articles","previous_headings":"Common use cases","what":"Intermittent infusion","title":"Patient records","text":"EVID = 1 bolus infusion, administered first compartment defined ppk model. DUR defines duration. AMT amount administered duration DUR.","code":"data.frame(ID=1,            TIME=c(0.0,1.0,14.0),            DV=c(NA,25.0,5.5),            AMT=c(1000,0,0),            DUR=c(0.5,NA,NA),            EVID=c(1,0,0),            COVAR1=c(\"X\"),            COVAR2=c(\"Y\")) #>   ID TIME   DV  AMT DUR EVID COVAR1 COVAR2 #> 1  1    0   NA 1000 0.5    1      X      Y #> 2  1    1 25.0    0  NA    0      X      Y #> 3  1   14  5.5    0  NA    0      X      Y"},{"path":"https://levenc.github.io/posologyr/articles/patient_data_input.html","id":"inter-occasion-variability","dir":"Articles","previous_headings":"Common use cases","what":"Inter-occasion variability","title":"Patient records","text":"given occasion, value OCC must repeated row. OCC must specified rows table.","code":"data.frame(ID=1,            TIME=c(0.0,1.0,14.0,24.0,25.0,36.0),            DV=c(NA,25.0,5.5,NA,30.0,6.0),            AMT=c(1000,0,0,1000,0,0),            DUR=c(0.5,NA,NA,0.5,NA,NA),            EVID=c(1,0,0,1,0,0),            OCC=c(1,1,1,2,2,2),            COVAR1=c(\"X\"),            COVAR2=c(\"Y\")) #>   ID TIME   DV  AMT DUR EVID OCC COVAR1 COVAR2 #> 1  1    0   NA 1000 0.5    1   1      X      Y #> 2  1    1 25.0    0  NA    0   1      X      Y #> 3  1   14  5.5    0  NA    0   1      X      Y #> 4  1   24   NA 1000 0.5    1   2      X      Y #> 5  1   25 30.0    0  NA    0   2      X      Y #> 6  1   36  6.0    0  NA    0   2      X      Y"},{"path":"https://levenc.github.io/posologyr/articles/patient_data_input.html","id":"multiple-endpoints","dir":"Articles","previous_headings":"Common use cases","what":"Multiple endpoints","title":"Patient records","text":"DVID used specify type observation. values DVID dataset must match names residual error models, see vignette(\"multiple_endpoints\").","code":"data.frame(ID=1,            TIME=c(0.0,1.0,14.0,24.0,25.0,36.0),            DV=c(NA,20.0,80,35.5,60.0,40.0),            AMT=c(1000,0,0,0,0,0),            EVID=c(1,0,0,0,0,0),            DVID=c(\"parent\",\"parent\",\"metabolite\",\"parent\",\"metabolite\",\"metabolite\"),            COVAR1=c(\"X\"),            COVAR2=c(\"Y\")) #>   ID TIME   DV  AMT EVID       DVID COVAR1 COVAR2 #> 1  1    0   NA 1000    1     parent      X      Y #> 2  1    1 20.0    0    0     parent      X      Y #> 3  1   14 80.0    0    0 metabolite      X      Y #> 4  1   24 35.5    0    0     parent      X      Y #> 5  1   25 60.0    0    0 metabolite      X      Y #> 6  1   36 40.0    0    0 metabolite      X      Y"},{"path":"https://levenc.github.io/posologyr/articles/population_models.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Population models","text":"describes structure prior population models compatible posologyr illustrates define new models published population models.","code":""},{"path":"https://levenc.github.io/posologyr/articles/population_models.html","id":"general-structure","dir":"Articles","previous_headings":"","what":"General structure","title":"Population models","text":"models can written model function can parsed rxode2. models written two block statements: ini({}) defining parameter values model({}) ODE-based model specification. example, gentamicin model Xuan et al. 2003 (doi:10.1016/j.ijantimicag.2003.07.010) can written : rxode2 mini-language syntax detailed rxode2 documentation.","code":"mod_gentamicin_Xuan2003 <- function() {   ini({     #Fixed effects: population estimates      THETA_Cl  = 0.047     THETA_V   = 0.28     THETA_k12 = 0.092     THETA_k21 = 0.071          #Random effects: inter-individual variability     ETA_Cl  ~ 0.084     ETA_V   ~ 0.003     ETA_k12 ~ 0.398     ETA_k21 ~ 0.342          #Unexplained residual variability     add_sd  <- 0.230     prop_sd <- 0.237   })   model({     #Individual model and covariates     TVl   = THETA_Cl*ClCr     TVV   = THETA_V*WT     TVk12 = THETA_k12     TVk21 = THETA_k21     Cl    = TVl*exp(ETA_Cl)     V     = TVV*exp(ETA_V)     k12   = TVk12*exp(ETA_k12)     k21   = TVk21*exp(ETA_k21)          #Structural model defined using ordinary differential equations (ODE)     ke    = Cl/V     Cp    = centr/V      d/dt(centr)  = - ke*centr - k12*centr + k21*periph     d/dt(periph) =            + k12*centr - k21*periph      #Model for unexplained residual variability     Cp ~ add(add_sd) + prop(prop_sd) + combined1()   }) }"},{"path":"https://levenc.github.io/posologyr/articles/population_models.html","id":"individual-model-random-effects","dir":"Articles","previous_headings":"","what":"Individual model, random effects","title":"Population models","text":"Inter-individual variability can also defined symmetric matrix integrate covariance random effects. example, amikacin model Burdet et al. 2015 (doi:10.1007/s00228-014-1766-y) can written : estimates variances random effects can given different parameterizations depending authors. Standard deviation (SD): square root variance, returned Monolix Coefficient variation (CV): calculated sqrt(exp(SD^2)-1), variance can computed back log((CV^2)+1) Full covariance matrix: easiest reuse, less common literature estimates covariance (diagonal) sometimes given coefficients correlation ETAs. covariance ETA_a ETA_b can computed following product: standard_deviation(ETA_a) * standard_deviation(ETA_b) * correlation(ETA_a ETA_b).","code":"mod_amikacin_Burdet2015 <- function() {     ini({       #Fixed effects: population estimates        THETA_Cl=4.3       THETA_Vc=15.9       THETA_Vp=21.4       THETA_Q=12.1              #Random effects: inter-individual variability       ETA_Cl + ETA_Vc + ETA_Vp + ETA_Q ~         c(0.1,           0.01     ,    0.05 ,           0.01     ,    0.02 ,   0.2  ,          -0.06     ,    0.004,   0.003,    0.08)                  #Unexplained residual variability       add_sd <- 0.2       prop_sd <- 0.1     })     model({       #Individual model and covariates       TVCl  = THETA_Cl*(CLCREAT4H/82)^0.7       TVVc  = THETA_Vc*(TBW/78)^0.9*(PoverF/169)^0.4       TVVp  = THETA_Vp       TVQ   = THETA_Q       Cl    = TVCl*exp(ETA_Cl)       Vc    = TVVc*exp(ETA_Vc)       Vp    = TVVp*exp(ETA_Vp)       Q     = TVQ *exp(ETA_Q)              #Structural model defined using ordinary differential equations (ODE)       ke    = Cl/Vc       k12   = Q/Vc       k21   = Q/Vp       Cp    = centr/Vc                    d/dt(centr)  = - ke*centr - k12*centr + k21*periph       d/dt(periph) =            + k12*centr - k21*periph        #Model for unexplained residual variability       Cp ~ add(add_sd) + prop(prop_sd) + combined1()     })   }"},{"path":"https://levenc.github.io/posologyr/articles/population_models.html","id":"bioavailability-lag-time","dir":"Articles","previous_headings":"","what":"Bioavailability, lag-time","title":"Population models","text":"Special model event changes including bioavailability (f(depot)=1), lag time (alag(depot)=0) can used model({}) block. example, ganciclovir model Caldès et al. 2009 (doi:10.1128/aac.00085-09) can written :","code":"mod_ganciclovir_Caldes2009 <- function() {   ini({     #Fixed effects: population estimates      THETA_cl  <- 7.49     THETA_v1  <- 31.90     THETA_cld <- 10.20     THETA_v2  <- 32.0     THETA_ka  <- 0.895     THETA_baf <- 0.825            #Random effects: inter-individual variability     ETA_cl ~ 0.107     ETA_v1 ~ 0.227     ETA_ka ~ 0.464     ETA_baf ~ 0.049          #Unexplained residual variability     add.sd <- 0.465     prop.sd <- 0.143   })   model({     #Individual model and covariates     TVcl  = THETA_cl*(ClCr/57);     TVv1  = THETA_v1;     TVcld = THETA_cld;     TVv2  = THETA_v2;     TVka  = THETA_ka;     TVbaf = THETA_baf;     cl  = TVcl*exp(ETA_cl);     v1  = TVv1*exp(ETA_v1);     cld = TVcld;     v2  = TVv2;     ka  = TVka*exp(ETA_ka);     baf = TVbaf*exp(ETA_baf);      #Structural model defined using ordinary differential equations (ODE)     k10 = cl/v1;     k12 = cld / v1;     k21 = cld / v2;     Cc = centr/v1;      d/dt(depot)  = -ka*depot     d/dt(centr)  =  ka*depot - k10*centr - k12*centr + k21*periph;     d/dt(periph) =                         k12*centr - k21*periph;     d/dt(AUC)    = Cc;      #Special model event changes     f(depot)=baf;     alag(depot)=0.382;          #Model for unexplained residual variability     Cc ~ add(add.sd) + prop(prop.sd) + combined1()   }) }"},{"path":"https://levenc.github.io/posologyr/articles/population_models.html","id":"classic-posologyr-models","dir":"Articles","previous_headings":"","what":"Classic posologyr models","title":"Population models","text":"models described rxode2 model function alone. possible define classic posologyr model, see vignette (\"classic_posologyr_models\"). Models falling category : models inter-occasion variability (IOV), sometimes called intra-individual variability models unexplained residual error model additive, proportional, combined models","code":""},{"path":"https://levenc.github.io/posologyr/articles/posologyr_user_defined_models.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"User defined models","text":"describes structure prior models usable posologyr illustrates define new models published population pharmacokinetic (ppk) models.","code":""},{"path":"https://levenc.github.io/posologyr/articles/posologyr_user_defined_models.html","id":"structure","dir":"Articles","previous_headings":"","what":"Structure","title":"User defined models","text":"posologyr prior ppk model named R list: ppk_model rxode2 model implementing structural population pharmacokinetics model individual model (.e. model inter-individual variability) covariates error_model function residual error model, alternatively named list functions multiple endpoints model vignette(\"multiple_endpoints\") theta named vector population estimates fixed effects parameters (called THETAs, following NONMEM terminology) omega named square variance-covariance matrix population parameters inter-individual variability sigma estimates parameters residual error model pi_matrix Optional. named square variance-covariance matrix population parameters inter-occasion variability covariates character vector covariates model","code":""},{"path":"https://levenc.github.io/posologyr/articles/posologyr_user_defined_models.html","id":"definition-of-a-prior-model-through-an-example","dir":"Articles","previous_headings":"","what":"Definition of a prior model through an example","title":"User defined models","text":"model implement two-compartment ppk model vancomycin derived retrospective study cohort 1,800 patients (doi:10.1097/FTD.0000000000000490).","code":""},{"path":"https://levenc.github.io/posologyr/articles/posologyr_user_defined_models.html","id":"ppk_model","dir":"Articles","previous_headings":"Definition of a prior model through an example","what":"ppk_model","title":"User defined models","text":"model defined rxode2::rxode() mini-language. posologyr needs structural model, defined either differential algebraic equations, individual model. concentration central compartment must named Cc. differential function d/dt(AUC) =  Cc; needed optimisation function poso_dose_auc().","code":"ppk_model   = rxode2::rxode({     centr(0) = 0;     TVCl  = THETA_Cl*(CLCREAT/120)^0.8*(0.7^DIAL);     TVVc  = THETA_Vc*(WT/70)          *(0.5^DIAL);     TVVp  = THETA_Vp;     TVQ   = THETA_Q;     Cl    = TVCl*exp(ETA_Cl);     Vc    = TVVc*exp(ETA_Vc);     Vp    = TVVp*exp(ETA_Vp);     Q     = TVQ;     ke    = Cl/Vc;     k12   = Q/Vc;     k21   = Q/Vp;     Cc    = centr/Vc;     d/dt(centr)  = - ke*centr - k12*centr + k21*periph;     d/dt(periph) =            + k12*centr - k21*periph;     d/dt(AUC)    =   Cc;   })"},{"path":"https://levenc.github.io/posologyr/articles/posologyr_user_defined_models.html","id":"error_model","dir":"Articles","previous_headings":"Definition of a prior model through an example","what":"error_model","title":"User defined models","text":"function residual error model, taking two arguments: simulated concentrations, vector sigma estimates parameters residual error model. Alternatively, function can take simulated concentrations, matrix sigma estimates parameters residual error model, following example: multiple endpoint models, error_model must named list function endpoint vignette(\"multiple_endpoints\").","code":"error_model <- function(f,sigma){     #additive model if sigma[2] == 0   g <- sigma[1] + sigma[2]*f          #proportional model if sigma[1] == 0   return(g) } error_model <- function(f,sigma){   dv <- cbind(f,1)   g  <- diag(dv%*%sigma%*%t(dv))     #sigma is the square matrix of the residual   return(sqrt(g))                    #errors }"},{"path":"https://levenc.github.io/posologyr/articles/posologyr_user_defined_models.html","id":"theta","dir":"Articles","previous_headings":"Definition of a prior model through an example","what":"theta","title":"User defined models","text":"estimations parameters fixed effects model (THETA), named vector. names must match names used ppk_model.","code":"theta = c(THETA_Cl=4.5, THETA_Vc=58.4, THETA_Vp=38.4, THETA_Q=6.5)"},{"path":"https://levenc.github.io/posologyr/articles/posologyr_user_defined_models.html","id":"omega","dir":"Articles","previous_headings":"Definition of a prior model through an example","what":"omega","title":"User defined models","text":"variance-covariance matrix random effects (ETA) individual model. symmetric matrix. names must match names used ppk_model. easy way define using lotri::lotri(). estimates variances random effects can given different parameterizations depending authors. Standard deviation (SD): square root variance, returned Monolix Coefficient variation (CV): calculated sqrt(exp(SD^2)-1), standard deviation can computed back sqrt(log((CV^2)+1)) Full covariance matrix: easiest reuse, rarely seen articles case vancomycin model, estimates subject variability (BSV) given CV%. must converted variances prior inclusion omega. estimates covariance (diagonal) sometimes given coefficients correlation ETAs. covariance ETA_a ETA_b can computed following product: standard_deviation(ETA_a) * standard_deviation(ETA_b) * correlation(ETA_a ETA_b). example, covariances equal zero.","code":"omega = lotri::lotri({ETA_Cl + ETA_Vc + ETA_Vp + ETA_Q ~                           c(0.147,                             0    ,  0.510 ,                             0    ,  0     ,   0.282,                             0    ,  0     ,   0    ,    0)})"},{"path":"https://levenc.github.io/posologyr/articles/posologyr_user_defined_models.html","id":"sigma","dir":"Articles","previous_headings":"Definition of a prior model through an example","what":"sigma","title":"User defined models","text":"estimates parameters residual error model, either vector: matrix: named list, see vignette(\"multiple_endpoints\"): depending residual error model.","code":"sigma       = c(additive_a = 3.4, proportional_b = 0.227) sigma       = lotri::lotri({prop + add ~ c(0.227,0.0,3.4)}) sigma       = list(     cp=c(additive_a = 0.144, proportional_b = 0.15),     pca=c(additive_a = 3.91, proportional_b = 0.0)     )"},{"path":"https://levenc.github.io/posologyr/articles/posologyr_user_defined_models.html","id":"pi_matrix","dir":"Articles","previous_headings":"Definition of a prior model through an example","what":"pi_matrix","title":"User defined models","text":"Optional: needed models inter-occasion variability (IOV). variance-covariance matrix random effects (KAPPA) IOV. omega matrix, names must match names used ppk_model. easy way define using lotri::lotri().","code":"pi_matrix = lotri::lotri({KAPPA_Cl + KAPPA_Vc ~       c(0.1934626,         0.00     ,  0.05783106)})"},{"path":"https://levenc.github.io/posologyr/articles/posologyr_user_defined_models.html","id":"covariates","dir":"Articles","previous_headings":"Definition of a prior model through an example","what":"covariates","title":"User defined models","text":"names every covariate defined ppk_model, character vector.","code":"covariates  = c(\"CLCREAT\",\"WT\",\"DIAL\")"},{"path":"https://levenc.github.io/posologyr/articles/posologyr_user_defined_models.html","id":"full-model","dir":"Articles","previous_headings":"Definition of a prior model through an example","what":"Full model","title":"User defined models","text":"posologyr model list objects. Note: model include inter-occasion variability, pi_matrix omitted.","code":"mod_vancomyin_Goti2018 <- list(   ppk_model   = rxode2::rxode({      centr(0) = 0;     TVCl  = THETA_Cl*(CLCREAT/120)^0.8*(0.7^DIAL);     TVVc  = THETA_Vc*(WT/70)          *(0.5^DIAL);     TVVp  = THETA_Vp;     TVQ   = THETA_Q;     Cl    = TVCl*exp(ETA_Cl);     Vc    = TVVc*exp(ETA_Vc);     Vp    = TVVp*exp(ETA_Vp);     Q     = TVQ;     ke    = Cl/Vc;     k12   = Q/Vc;     k21   = Q/Vp;     Cc    = centr/Vc;     d/dt(centr)  = - ke*centr - k12*centr + k21*periph;     d/dt(periph) =            + k12*centr - k21*periph;     d/dt(AUC)    =   Cc;   }),   error_model = function(f,sigma){     g <- sigma[1] + sigma[2]*f     return(g)   },   theta = c(THETA_Cl=4.5, THETA_Vc=58.4, THETA_Vp=38.4,THETA_Q=6.5),   omega = lotri::lotri({ETA_Cl + ETA_Vc + ETA_Vp + ETA_Q ~                           c(0.147,                             0    ,  0.510 ,                             0    ,  0     ,   0.282,                             0    ,  0     ,   0    ,    0)}),   sigma       = c(additive_a = 3.4, proportional_b = 0.227),   covariates  = c(\"CLCREAT\",\"WT\",\"DIAL\"))"},{"path":"https://levenc.github.io/posologyr/articles/posologyr_user_defined_models.html","id":"resulting-r-object","dir":"Articles","previous_headings":"Definition of a prior model through an example > Full model","what":"Resulting R object","title":"User defined models","text":"","code":"mod_vancomyin_Goti2018 #> $ppk_model #> rxode2 NA model named rx_bce7176cfa18100af62e71ae38d3cd48 model (✔ ready).  #> $state: centr, periph, AUC #> $params: THETA_Cl, CLCREAT, DIAL, THETA_Vc, WT, THETA_Vp, THETA_Q, ETA_Cl, ETA_Vc, ETA_Vp #> $lhs: TVCl, TVVc, TVVp, TVQ, Cl, Vc, Vp, Q, ke, k12, k21, Cc #>  #> $error_model #> function(f,sigma){ #>     g <- sigma[1] + sigma[2]*f #>     return(g) #>   } #>  #> $theta #> THETA_Cl THETA_Vc THETA_Vp  THETA_Q  #>      4.5     58.4     38.4      6.5  #>  #> $omega #>        ETA_Cl ETA_Vc ETA_Vp ETA_Q #> ETA_Cl  0.147   0.00  0.000     0 #> ETA_Vc  0.000   0.51  0.000     0 #> ETA_Vp  0.000   0.00  0.282     0 #> ETA_Q   0.000   0.00  0.000     0 #>  #> $sigma #>     additive_a proportional_b  #>          3.400          0.227  #>  #> $covariates #> [1] \"CLCREAT\" \"WT\"      \"DIAL\""},{"path":"https://levenc.github.io/posologyr/articles/route_of_administration.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Route of administration","text":"Caldès 2009 ganciclovir model (https://doi.org/10.1128/aac.00085-09) capable describing pharmacokinetics either injectable ganciclovir oral valganciclovir.","code":"mod_ganciclovir_Caldes_2009 <- function() {   ini({     THETA_cl  <- 7.49     THETA_v1  <- 31.90     THETA_cld <- 10.20     THETA_v2  <- 32.0     THETA_ka  <- 0.895     THETA_baf <- 0.825     ETA_cl ~ 0.107     ETA_v1 ~ 0.227     ETA_ka ~ 0.464     ETA_baf ~ 0.049     add.sd <- 0.465     prop.sd <- 0.143   })   model({     TVcl  = THETA_cl*(ClCr/57);     TVv1  = THETA_v1;     TVcld = THETA_cld;     TVv2  = THETA_v2;     TVka  = THETA_ka;     TVbaf = THETA_baf;      cl  = TVcl*exp(ETA_cl);     v1  = TVv1*exp(ETA_v1);     cld = TVcld;     v2  = TVv2;     ka  = TVka*exp(ETA_ka);     baf = TVbaf*exp(ETA_baf);      k10 = cl/v1;     k12 = cld / v1;     k21 = cld / v2;     Cc = centr/v1;      d/dt(depot)  = -ka*depot     d/dt(centr)  =  ka*depot - k10*centr - k12*centr + k21*periph;     d/dt(periph) =                         k12*centr - k21*periph;     d/dt(AUC)    = Cc;      f(depot)=baf;     alag(depot)=0.382;      Cc ~ add(add.sd) + prop(prop.sd) + combined1()   }) }"},{"path":[]},{"path":"https://levenc.github.io/posologyr/articles/route_of_administration.html","id":"patient-record-with-tdm-data","dir":"Articles","previous_headings":"Intravenous ganciclovir","what":"Patient record with TDM data","title":"Route of administration","text":"describe intravenous administration, CMT column added TDM data table indicate administrations directly central compartment. Note: compute AUC last dose time last dose + 24 hours, dummy dose 0 mg added time last observation interest (.e. H144).","code":"patient <- data.frame(ID=1,TIME=c(0,121,122,126,144),                       DV=c(NA,10.8,5.8,3.3,NA),                       ADDL=c(5,0,0,0,0),                       II=c(24,0,0,0,0),                       EVID=c(1,0,0,0,1),                       CMT=c(\"centr\",NA,NA,NA,\"centr\"),                       AMT=c(250,0,0,0,0),                       DUR=c(0.5,NA,NA,NA,NA),                       ClCr=25) patient #>   ID TIME   DV ADDL II EVID   CMT AMT DUR ClCr #> 1  1    0   NA    5 24    1 centr 250 0.5   25 #> 2  1  121 10.8    0  0    0  <NA>   0  NA   25 #> 3  1  122  5.8    0  0    0  <NA>   0  NA   25 #> 4  1  126  3.3    0  0    0  <NA>   0  NA   25 #> 5  1  144   NA    0  0    1 centr   0  NA   25"},{"path":"https://levenc.github.io/posologyr/articles/route_of_administration.html","id":"individual-pk-profile-and-auc-0-24","dir":"Articles","previous_headings":"Intravenous ganciclovir","what":"Individual PK profile and AUC 0-24","title":"Route of administration","text":"individual PK profile can estimated, plotted.  difference cumulative AUC H144 H120 gives AUC 0-24 last dose. Using data.table optional, syntax convenient.","code":"map_patient <- poso_estim_map(patient,mod_ganciclovir_Caldes_2009) plot(map_patient$model,Cc) library(data.table) data.table(map_patient$model)[time==144,AUC] -    data.table(map_patient$model)[time==120,AUC] #> [1] 72.19085"},{"path":"https://levenc.github.io/posologyr/articles/route_of_administration.html","id":"optimal-dose-for-an-intravenous-ganciclovir-injection","dir":"Articles","previous_headings":"Intravenous ganciclovir","what":"Optimal dose for an intravenous ganciclovir injection","title":"Route of administration","text":"optimal dose achieve AUC 50 mg.h/L can determined new injection IV ganciclovir setting cmt_dose = \"centr\".","code":"poso_dose_auc(patient,mod_ganciclovir_Caldes_2009,tdm=TRUE,               time_dose = 145,               duration = 1,               time_auc = 24,               target_auc = 50,               cmt_dose = \"centr\") #> $dose #> [1] 156.5335 #>  #> $type_of_estimate #> [1] \"point estimate\" #>  #> $auc_estimate #> [1] 50 #>  #> $indiv_param #>   THETA_cl THETA_v1 THETA_cld THETA_v2 THETA_ka THETA_baf add.sd prop.sd #> 1     7.49     31.9      10.2       32    0.895     0.825  0.465   0.143 #>       ETA_cl     ETA_v1        ETA_ka       ETA_baf covar #> 1 0.05256541 -0.4773341 -3.589527e-08 -1.272466e-07    25"},{"path":"https://levenc.github.io/posologyr/articles/route_of_administration.html","id":"optimal-dose-for-an-oral-valganciclovir-administration","dir":"Articles","previous_headings":"Intravenous ganciclovir","what":"Optimal dose for an oral valganciclovir administration","title":"Route of administration","text":"optimal dose achieve AUC 50 mg.h/L can determined administration oral valganciclovir setting cmt_dose = \"depot\". Keeping default value cmt_dose, first compartment declared PK model, also work .","code":"poso_dose_auc(patient,mod_ganciclovir_Caldes_2009,tdm=TRUE,               time_dose = 145,               time_auc = 24,               target_auc = 50,               cmt_dose = \"depot\") #> $dose #> [1] 193.1298 #>  #> $type_of_estimate #> [1] \"point estimate\" #>  #> $auc_estimate #> [1] 50 #>  #> $indiv_param #>   THETA_cl THETA_v1 THETA_cld THETA_v2 THETA_ka THETA_baf add.sd prop.sd #> 1     7.49     31.9      10.2       32    0.895     0.825  0.465   0.143 #>      ETA_cl     ETA_v1        ETA_ka      ETA_baf covar #> 1 0.0525648 -0.4773328 -1.018546e-06 2.327066e-07    25"},{"path":"https://levenc.github.io/posologyr/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Cyril Leven. Author, maintainer, copyright holder. Matthew Fidler. Contributor. Emmanuelle Comets. Contributor. Audrey Lavenu. Contributor. Marc Lavielle. Contributor.","code":""},{"path":"https://levenc.github.io/posologyr/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Leven C, Coste , Mané C (2022). “Free Open-Source Posologyr Software Bayesian Dose Individualization: Extensive Validation Simulated Data.” Pharmaceutics, 14(2), 442. doi:10.3390/pharmaceutics14020442, https://europepmc.org/article/pmc/8879752.","code":"@Article{posologyrpaper,   title = {Free and Open-Source Posologyr Software for Bayesian Dose Individualization: An Extensive Validation on Simulated Data},   author = {Cyril Leven and Anne Coste and Camille Mané},   journal = {Pharmaceutics},   year = {2022},   volume = {14},   pages = {442},   number = {2},   doi = {10.3390/pharmaceutics14020442},   publisher = {MDPI},   url = {https://europepmc.org/article/pmc/8879752}, }"},{"path":[]},{"path":"https://levenc.github.io/posologyr/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Individual Dose Optimization using Population Pharmacokinetics","text":"Personalize drug regimens using individual pharmacokinetic (PK) pharmacokinetic-pharmacodynamic (PK-PD) profiles. combining therapeutic drug monitoring (TDM) data population model, posologyr offers accurate posterior estimates helps compute optimal individualized dosing regimens. Key dosage optimization functions posologyr include: poso_dose_conc() estimates optimal dose achieve target concentration given time poso_dose_auc() estimates dose needed reach target area concentration-time curve (AUC) poso_time_cmin() estimates time required reach target trough concentration (Cmin) poso_inter_cmin() estimates optimal dosing interval consistently achieve target Cmin Individual PK profiles can estimated without TDM data: poso_estim_map() computes Maximum Posteriori Bayesian Estimates (MAP-) individual PK parameters using TDM results poso_simu_pop() samples prior distributions PK parameters posologyr leverages simulation capabilities rxode2 package.","code":""},{"path":"https://levenc.github.io/posologyr/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Individual Dose Optimization using Population Pharmacokinetics","text":"can install released version posologyr CRAN : can install development version posologyr GitHub :","code":"install.packages(\"posologyr\") # install.packages(\"remotes\") remotes::install_github(\"levenc/posologyr\")"},{"path":"https://levenc.github.io/posologyr/index.html","id":"bayesian-dosing-example","dir":"","previous_headings":"","what":"Bayesian dosing example","title":"Individual Dose Optimization using Population Pharmacokinetics","text":"determine optimal dose gentamicin patient posologyr, need: prior PK model, written rxode2 mini-language example, gentamicin PK literature doi:10.1016/j.ijantimicag.2003.07.010 table patient’s TDM data, format similar data NONMEM","code":"mod_gentamicin_Xuan2003 <- function() {   ini({     THETA_Cl  = 0.047     THETA_V   = 0.28     THETA_k12 = 0.092     THETA_k21 = 0.071     ETA_Cl  ~ 0.084     ETA_V   ~ 0.003     ETA_k12 ~ 0.398     ETA_k21 ~ 0.342     add_sd  <- 0.230     prop_sd <- 0.237   })   model({     TVl   = THETA_Cl*ClCr     TVV   = THETA_V*WT     TVk12 = THETA_k12     TVk21 = THETA_k21          Cl    = TVl*exp(ETA_Cl)     V     = TVV*exp(ETA_V)     k12   = TVk12*exp(ETA_k12)     k21   = TVk21 *exp(ETA_k21)          ke    = Cl/V     Cp    = centr/V          d/dt(centr)  = - ke*centr - k12*centr + k21*periph     d/dt(periph) =            + k12*centr - k21*periph      Cp ~ add(add_sd) + prop(prop_sd) + combined1()   }) } patient_data <- data.frame(ID=1,                            TIME=c(0.0,1.0,11.0),                            DV=c(NA,9,2),                            AMT=c(180,0,0),                            DUR=c(0.5,NA,NA),                            EVID=c(1,0,0),                            ClCr=38,                            WT=63) patient_data #>   ID TIME DV AMT DUR EVID ClCr WT #> 1  1    0 NA 180 0.5    1   38 63 #> 2  1    1  9   0  NA    0   38 63 #> 3  1   11  2   0  NA    0   38 63"},{"path":"https://levenc.github.io/posologyr/index.html","id":"individual-pk-profile","dir":"","previous_headings":"Bayesian dosing example","what":"Individual PK profile","title":"Individual Dose Optimization using Population Pharmacokinetics","text":"two elements, can estimate plot individual concentrations time.","code":"library(\"posologyr\") patient_map <- poso_estim_map(patient_data,mod_gentamicin_Xuan2003) plot(patient_map$model,Cc)"},{"path":"https://levenc.github.io/posologyr/index.html","id":"dose-optimization","dir":"","previous_headings":"Bayesian dosing example","what":"Dose optimization","title":"Individual Dose Optimization using Population Pharmacokinetics","text":"optimize gentamicin dosage patient meet two criteria: peak concentration 12 mg/L, 30 minutes 30-minute infusion. trough concentration less 0.5 mg/L. time required reach residual concentration 0.5 mg/L can estimated follows: dose required achieve target concentration can determined infusion H48. conclusion dose 240 mg 48 h first injection appropriate meet 2 criteria. examples can found : https://levenc.github.io/posologyr/","code":"poso_time_cmin(patient_data,mod_gentamicin_Xuan2003,tdm=TRUE,                target_cmin = 0.5) #> $time #> [1] 44.9 #>  #> $type_of_estimate #> [1] \"point estimate\" #>  #> $cmin_estimate #> [1] 0.4991313 #>  #> $indiv_param #>   THETA_Cl THETA_V THETA_k12 THETA_k21 add_sd prop_sd     ETA_Cl       ETA_V #> 3    0.047    0.28     0.092     0.071   0.23   0.237 0.03701064 0.001447308 #>      ETA_k12     ETA_k21 ClCr WT #> 3 0.08904703 -0.04838898   38 63 poso_dose_conc(patient_data,mod_gentamicin_Xuan2003,tdm=TRUE,                target_conc = 12,duration=0.5,time_dose = 48,time_c = 49) #> $dose #> [1] 237.5902 #>  #> $type_of_estimate #> [1] \"point estimate\" #>  #> $conc_estimate #> [1] 12 #>  #> $indiv_param #>   THETA_Cl THETA_V THETA_k12 THETA_k21 add_sd prop_sd     ETA_Cl       ETA_V #> 3    0.047    0.28     0.092     0.071   0.23   0.237 0.03701052 0.001447305 #>      ETA_k12     ETA_k21 ClCr WT #> 3 0.08904752 -0.04838936   38 63"},{"path":"https://levenc.github.io/posologyr/index.html","id":"performance-of-the-map-be-algorithm-in-posologyr","dir":"","previous_headings":"","what":"Performance of the MAP-BE algorithm in posologyr","title":"Individual Dose Optimization using Population Pharmacokinetics","text":"posologyr showed comparable performance NONMEM MAP estimation option MAXEVAL=0: Pharmaceutics 2022, 14(2), 442; doi:10.3390/pharmaceutics14020442 Supporting data: https://github.com/levenc/posologyr-pharmaceutics","code":""},{"path":"https://levenc.github.io/posologyr/reference/error_model_comb1.html","id":null,"dir":"Reference","previous_headings":"","what":"Residual error model combined 1 — error_model_comb1","title":"Residual error model combined 1 — error_model_comb1","text":"Residual error model combined 1. Constant error model proportional coefficient provided. Proportional error model constant (additive) error coefficient provided.","code":""},{"path":"https://levenc.github.io/posologyr/reference/error_model_comb1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Residual error model combined 1 — error_model_comb1","text":"","code":"error_model_comb1(f, sigma)"},{"path":"https://levenc.github.io/posologyr/reference/error_model_comb1.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Residual error model combined 1 — error_model_comb1","text":"f Numeric vector, output pharmacokinetic model sigma Numeric vector coefficients residual error model","code":""},{"path":"https://levenc.github.io/posologyr/reference/error_model_comb1.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Residual error model combined 1 — error_model_comb1","text":"Numeric vector, residual error","code":""},{"path":"https://levenc.github.io/posologyr/reference/error_model_comb1.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Residual error model combined 1 — error_model_comb1","text":"Implements following function: g <- sigma[1] + sigma[2]*f","code":""},{"path":"https://levenc.github.io/posologyr/reference/error_model_comb2.html","id":null,"dir":"Reference","previous_headings":"","what":"Residual error model combined 2 — error_model_comb2","title":"Residual error model combined 2 — error_model_comb2","text":"Residual error model combined 2.","code":""},{"path":"https://levenc.github.io/posologyr/reference/error_model_comb2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Residual error model combined 2 — error_model_comb2","text":"","code":"error_model_comb2(f, sigma)"},{"path":"https://levenc.github.io/posologyr/reference/error_model_comb2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Residual error model combined 2 — error_model_comb2","text":"f Numeric vector, output pharmacokinetic model sigma Numeric vector coefficients residual error model","code":""},{"path":"https://levenc.github.io/posologyr/reference/error_model_comb2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Residual error model combined 2 — error_model_comb2","text":"Numeric vector, residual error","code":""},{"path":"https://levenc.github.io/posologyr/reference/error_model_comb2.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Residual error model combined 2 — error_model_comb2","text":"Implements following function: g <- sqrt(sigma[1]^2 + sigma[2]^2*f^2)","code":""},{"path":"https://levenc.github.io/posologyr/reference/error_model_mixednm.html","id":null,"dir":"Reference","previous_headings":"","what":"Residual error model mixed (idem NONMEM) — error_model_mixednm","title":"Residual error model mixed (idem NONMEM) — error_model_mixednm","text":"Mixed residual error model, similar NONMEM implementation.","code":""},{"path":"https://levenc.github.io/posologyr/reference/error_model_mixednm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Residual error model mixed (idem NONMEM) — error_model_mixednm","text":"","code":"error_model_mixednm(f, sigma)"},{"path":"https://levenc.github.io/posologyr/reference/error_model_mixednm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Residual error model mixed (idem NONMEM) — error_model_mixednm","text":"f Numeric vector, output pharmacokinetic model sigma Matrix coefficients residual error model","code":""},{"path":"https://levenc.github.io/posologyr/reference/error_model_mixednm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Residual error model mixed (idem NONMEM) — error_model_mixednm","text":"Numeric vector, residual error","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_dose_auc.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the dose needed to reach a target area under the concentration-time curve (AUC) — poso_dose_auc","title":"Estimate the dose needed to reach a target area under the concentration-time curve (AUC) — poso_dose_auc","text":"estimates dose needed reach target area concentration-time curve (AUC) given population pharmacokinetic model, set individual parameters, target AUC.","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_dose_auc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the dose needed to reach a target area under the concentration-time curve (AUC) — poso_dose_auc","text":"","code":"poso_dose_auc(   dat = NULL,   prior_model = NULL,   tdm = FALSE,   time_auc,   time_dose = NULL,   cmt_dose = 1,   target_auc,   estim_method = \"map\",   nocb = FALSE,   p = NULL,   greater_than = TRUE,   starting_time = 0,   interdose_interval = NULL,   add_dose = NULL,   duration = 0,   starting_dose = 100,   indiv_param = NULL )"},{"path":"https://levenc.github.io/posologyr/reference/poso_dose_auc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the dose needed to reach a target area under the concentration-time curve (AUC) — poso_dose_auc","text":"dat Dataframe. individual subject dataset following structure NONMEM/rxode2 event records. prior_model posologyr prior population pharmacokinetics model, list six objects. tdm boolean. TRUE: estimates optimal dose selected target auc selected duration following events dat, using Maximum Posteriori estimation. Setting tdm TRUE causes following occur: time_dose argument required used starting point AUC calculation instead starting_time; arguments estim_method, p, greater_than, interdose_interval, add_dose, indiv_param starting_time ignored. time_auc Numeric. duration. target AUC computed starting_time starting_time + time_auc. tdm set TRUE target AUC computed time_dose time_dose + time_auc instead. time_dose Numeric. Time dose given. used mandatory, tdm set TRUE. cmt_dose Character numeric. compartment dose administered. Must match one compartments prior model. Defaults 1. target_auc Numeric. target AUC. estim_method character string. estimation method used individual parameters. default method \"map\" Maximum Posteriori estimation, method \"prior\" simulates prior population model, \"sir\" uses Sequential Importance Resampling algorithm estimate posteriori distribution individual parameters. argument ignored indiv_param provided, tdm set TRUE. nocb boolean. time-varying covariates: next observation carried backward (nocb) interpolation style, similar NONMEM.  FALSE, last observation carried forward (locf) style used. Defaults FALSE. p Numeric. proportion distribution AUC consider optimization. Mandatory estim_method=sir. argument ignored tdm set TRUE. greater_than boolean. TRUE: targets dose leading proportion p AUCs greater target_auc. Respectively, lower FALSE. argument ignored tdm set TRUE. starting_time Numeric. First point time AUC, multiple dose regimen. default zero. argument ignored tdm set TRUE, time_dose used starting point instead. interdose_interval Numeric. Time interdose interval multiple dose regimen. Must provided add_dose used. argument ignored tdm set TRUE. add_dose Numeric. Additional doses administered inter-dose interval first dose. Optional. argument ignored tdm set TRUE. duration Numeric. Duration infusion, zero-order administrations. starting_dose Numeric. Starting dose optimization algorithm. indiv_param Optional. set individual parameters : THETA, estimates ETA, covariates. argument ignored tdm set TRUE.","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_dose_auc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the dose needed to reach a target area under the concentration-time curve (AUC) — poso_dose_auc","text":"list containing following components: dose Numeric. optimal dose selected target AUC. type_of_estimate Character string. type estimate individual parameters. Either point estimate, distribution. auc_estimate vector numeric estimates AUC. Either single value (point estimate ETA), distribution. indiv_param data.frame. set individual parameters used determination optimal dose : THETA, estimates ETA, covariates","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_dose_auc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate the dose needed to reach a target area under the concentration-time curve (AUC) — poso_dose_auc","text":"","code":"rxode2::setRxThreads(2L) # limit the number of threads  # model mod_run001 <- function() {   ini({     THETA_Cl <- 4.0     THETA_Vc <- 70.0     THETA_Ka <- 1.0     ETA_Cl ~ 0.2     ETA_Vc ~ 0.2     ETA_Ka ~ 0.2     prop.sd <- sqrt(0.05)   })   model({     TVCl <- THETA_Cl     TVVc <- THETA_Vc     TVKa <- THETA_Ka      Cl <- TVCl*exp(ETA_Cl)     Vc <- TVVc*exp(ETA_Vc)     Ka <- TVKa*exp(ETA_Ka)      K20 <- Cl/Vc     Cc <- centr/Vc      d/dt(depot) = -Ka*depot     d/dt(centr) = Ka*depot - K20*centr     Cc ~ prop(prop.sd)   }) } # df_patient01: event table for Patient01, following a 30 minutes intravenous # infusion df_patient01 <- data.frame(ID=1,                         TIME=c(0.0,1.0,14.0),                         DV=c(NA,25.0,5.5),                         AMT=c(2000,0,0),                         EVID=c(1,0,0),                         DUR=c(0.5,NA,NA)) # estimate the optimal dose to reach an AUC(0-12h) of 45 h.mg/l poso_dose_auc(dat=df_patient01,prior_model=mod_run001, time_auc=12,target_auc=45) #>   #>   #>   #>   #> $dose #> [1] 396.0027 #>  #> $type_of_estimate #> [1] \"point estimate\" #>  #> $auc_estimate #> [1] 45 #>  #> $indiv_param #>   THETA_Cl THETA_Vc THETA_Ka   prop.sd    ETA_Cl     ETA_Vc    ETA_Ka #> 1        4       70        1 0.2236068 0.6018995 -0.4291782 0.1278321 #>"},{"path":"https://levenc.github.io/posologyr/reference/poso_dose_conc.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the optimal dose to achieve a target concentration at any given time — poso_dose_conc","title":"Estimate the optimal dose to achieve a target concentration at any given time — poso_dose_conc","text":"Estimates optimal dose achieve target concentration given time given population pharmacokinetic model, set individual parameters, selected point time, target concentration.","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_dose_conc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the optimal dose to achieve a target concentration at any given time — poso_dose_conc","text":"","code":"poso_dose_conc(   dat = NULL,   prior_model = NULL,   tdm = FALSE,   time_c,   time_dose = NULL,   target_conc,   cmt_dose = 1,   endpoint = \"Cc\",   estim_method = \"map\",   nocb = FALSE,   p = NULL,   greater_than = TRUE,   starting_dose = 100,   interdose_interval = NULL,   add_dose = NULL,   duration = 0,   indiv_param = NULL )"},{"path":"https://levenc.github.io/posologyr/reference/poso_dose_conc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the optimal dose to achieve a target concentration at any given time — poso_dose_conc","text":"dat Dataframe. individual subject dataset following structure NONMEM/rxode2 event records. prior_model posologyr prior population pharmacokinetics model, list six objects. tdm boolean. TRUE: estimates optimal dose selected target concentration selected point time following events dat, using Maximum Posteriori estimation. Setting tdm TRUE causes following occur: arguments estim_method, p, greater_than, interdose_interval, add_dose, indiv_param starting_time ignored. time_c Numeric. Point time dose optimized. time_dose Numeric. Time dose given. target_conc Numeric. Target concentration. cmt_dose Character numeric. compartment dose administered. Must match one compartments prior model. Defaults 1. endpoint Character. endpoint prior model optimised . default \"Cc\", central concentration. estim_method character string. estimation method used individual parameters. default method \"map\" Maximum Posteriori estimation, method \"prior\" simulates prior population model, \"sir\" uses Sequential Importance Resampling algorithm estimate posteriori distribution individual parameters. argument ignored indiv_param provided tdm set TRUE. nocb boolean. time-varying covariates: next observation carried backward (nocb) interpolation style, similar NONMEM.  FALSE, last observation carried forward (locf) style used. Defaults FALSE. p Numeric. proportion distribution concentrations consider optimization. Mandatory estim_method=sir. argument ignored tdm set TRUE. greater_than boolean. TRUE: targets dose leading proportion p concentrations greater target_conc. Respectively, lower FALSE. argument ignored tdm set TRUE. starting_dose Numeric. Starting dose optimization algorithm. interdose_interval Numeric. Time interdose interval multiple dose regimen. Must provided add_dose used. argument ignored tdm set TRUE. add_dose Numeric. Additional doses administered inter-dose interval first dose. Optional. argument ignored tdm set TRUE. duration Numeric. Duration infusion, zero-order administrations. indiv_param Optional. set individual parameters : THETA, estimates ETA, covariates. argument ignored tdm set TRUE.","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_dose_conc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the optimal dose to achieve a target concentration at any given time — poso_dose_conc","text":"list containing following components: dose Numeric. optimal dose selected target concentration. type_of_estimate Character string. type estimate individual parameters. Either point estimate, distribution. conc_estimate vector numeric estimates conc. Either single value (point estimate ETA), distribution. indiv_param data.frame. set individual parameters used determination optimal dose : THETA, estimates ETA, covariates","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_dose_conc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate the optimal dose to achieve a target concentration at any given time — poso_dose_conc","text":"","code":"rxode2::setRxThreads(2L) # limit the number of threads  # model mod_run001 <- function() {   ini({     THETA_Cl <- 4.0     THETA_Vc <- 70.0     THETA_Ka <- 1.0     ETA_Cl ~ 0.2     ETA_Vc ~ 0.2     ETA_Ka ~ 0.2     prop.sd <- sqrt(0.05)   })   model({     TVCl <- THETA_Cl     TVVc <- THETA_Vc     TVKa <- THETA_Ka      Cl <- TVCl*exp(ETA_Cl)     Vc <- TVVc*exp(ETA_Vc)     Ka <- TVKa*exp(ETA_Ka)      K20 <- Cl/Vc     Cc <- centr/Vc      d/dt(depot) = -Ka*depot     d/dt(centr) = Ka*depot - K20*centr     Cc ~ prop(prop.sd)   }) } # df_patient01: event table for Patient01, following a 30 minutes intravenous # infusion df_patient01 <- data.frame(ID=1,                         TIME=c(0.0,1.0,14.0),                         DV=c(NA,25.0,5.5),                         AMT=c(2000,0,0),                         EVID=c(1,0,0),                         DUR=c(0.5,NA,NA)) # estimate the optimal dose to reach a concentration of 80 mg/l # one hour after starting the 30-minutes infusion poso_dose_conc(dat=df_patient01,prior_model=mod_run001, time_c=1,duration=0.5,target_conc=80) #>   #>   #>   #>   #> $dose #> [1] 6886.024 #>  #> $type_of_estimate #> [1] \"point estimate\" #>  #> $conc_estimate #> [1] 80 #>  #> $indiv_param #>   THETA_Cl THETA_Vc THETA_Ka   prop.sd    ETA_Cl     ETA_Vc    ETA_Ka #> 1        4       70        1 0.2236068 0.6019041 -0.4291723 0.1278484 #>"},{"path":"https://levenc.github.io/posologyr/reference/poso_estim_map.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Maximum A Posteriori individual parameters — poso_estim_map","title":"Estimate the Maximum A Posteriori individual parameters — poso_estim_map","text":"Estimates Maximum Posteriori (MAP) individual parameters, also known Empirical Bayes Estimates (EBE).","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_estim_map.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Maximum A Posteriori individual parameters — poso_estim_map","text":"","code":"poso_estim_map(   dat = NULL,   prior_model = NULL,   return_model = TRUE,   return_ofv = FALSE,   nocb = FALSE )"},{"path":"https://levenc.github.io/posologyr/reference/poso_estim_map.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Maximum A Posteriori individual parameters — poso_estim_map","text":"dat Dataframe. individual subject dataset following structure NONMEM/rxode2 event records. prior_model posologyr prior population pharmacokinetics model, list six objects. return_model boolean. Returns rxode2 model using estimated ETAs set TRUE. return_ofv boolean. Returns Objective Function Value (OFV) set TRUE. nocb boolean. time-varying covariates: next observation carried backward (nocb) interpolation style, similar NONMEM.  FALSE, last observation carried forward (locf) style used. Defaults FALSE.","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_estim_map.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Maximum A Posteriori individual parameters — poso_estim_map","text":"named list consisting one following elements depending input parameters function: $eta named vector MAP estimates individual values ETA, $model rxode2 model using estimated ETAs, $event data.table used solve returned rxode2 model.","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_estim_map.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate the Maximum A Posteriori individual parameters — poso_estim_map","text":"","code":"rxode2::setRxThreads(1) # limit the number of threads  # model mod_run001 <- function() {   ini({     THETA_Cl <- 4.0     THETA_Vc <- 70.0     THETA_Ka <- 1.0     ETA_Cl ~ 0.2     ETA_Vc ~ 0.2     ETA_Ka ~ 0.2     prop.sd <- sqrt(0.05)   })   model({     TVCl <- THETA_Cl     TVVc <- THETA_Vc     TVKa <- THETA_Ka      Cl <- TVCl*exp(ETA_Cl)     Vc <- TVVc*exp(ETA_Vc)     Ka <- TVKa*exp(ETA_Ka)      K20 <- Cl/Vc     Cc <- centr/Vc      d/dt(depot) = -Ka*depot     d/dt(centr) = Ka*depot - K20*centr     Cc ~ prop(prop.sd)   }) } # df_patient01: event table for Patient01, following a 30 minutes intravenous # infusion df_patient01 <- data.frame(ID=1,                         TIME=c(0.0,1.0,14.0),                         DV=c(NA,25.0,5.5),                         AMT=c(2000,0,0),                         EVID=c(1,0,0),                         DUR=c(0.5,NA,NA)) # estimate the Maximum A Posteriori individual parameters poso_estim_map(dat=df_patient01,prior_model=mod_run001) #>   #>   #>   #>   #> $eta #>     ETA_Cl     ETA_Vc     ETA_Ka  #>  0.6019038 -0.4291730  0.1278482  #>  #> $model #> ── Solved rxode2 object ── #> ── Parameters ($params): ── #>   THETA_Cl   THETA_Vc   THETA_Ka    prop.sd     ETA_Cl     ETA_Vc     ETA_Ka  #>  4.0000000 70.0000000  1.0000000  0.2236068  0.6019038 -0.4291730  0.1278482  #> ── Initial Conditions ($inits): ── #> depot centr   AUC  #>     0     0     0  #> ── First part of data (object): ── #> # A tibble: 151 × 13 #>    time  TVCl  TVVc  TVKa    Cl    Vc    Ka   K20   rxCc     Cc depot centr #>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>  <dbl> <dbl> <dbl> #> 1   0       4    70     1  7.30  45.6  1.14 0.160  0      0        0    0   #> 2   0.1     4    70     1  7.30  45.6  1.14 0.160  0.478  0.478  378.  21.8 #> 3   0.2     4    70     1  7.30  45.6  1.14 0.160  1.83   1.83   716.  83.5 #> 4   0.3     4    70     1  7.30  45.6  1.14 0.160  3.95   3.95  1017. 180.  #> 5   0.4     4    70     1  7.30  45.6  1.14 0.160  6.75   6.75  1286. 307.  #> 6   0.5     4    70     1  7.30  45.6  1.14 0.160 10.1   10.1   1526. 461.  #> # ℹ 145 more rows #> # ℹ 1 more variable: AUC <dbl> #>  #> $event #>         id  time   amt  evid   dur #>      <int> <num> <num> <int> <num> #>   1:     1   0.0    NA     0    NA #>   2:     1   0.0  2000     1   0.5 #>   3:     1   0.1    NA     0    NA #>   4:     1   0.2    NA     0    NA #>   5:     1   0.3    NA     0    NA #>  ---                               #> 148:     1  14.6    NA     0    NA #> 149:     1  14.7    NA     0    NA #> 150:     1  14.8    NA     0    NA #> 151:     1  14.9    NA     0    NA #> 152:     1  15.0    NA     0    NA #>"},{"path":"https://levenc.github.io/posologyr/reference/poso_estim_mcmc.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the posterior distribution of individual parameters by MCMC — poso_estim_mcmc","title":"Estimate the posterior distribution of individual parameters by MCMC — poso_estim_mcmc","text":"Estimates posterior distribution individual parameters Markov Chain Monte Carlo (using Metropolis-Hastings algorithm)","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_estim_mcmc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the posterior distribution of individual parameters by MCMC — poso_estim_mcmc","text":"","code":"poso_estim_mcmc(   dat = NULL,   prior_model = NULL,   return_model = TRUE,   burn_in = 50,   n_iter = 1000,   n_chains = 4,   nocb = FALSE,   control = list(n_kernel = c(2, 2, 2), stepsize_rw = 0.4, proba_mcmc = 0.3, nb_max = 3) )"},{"path":"https://levenc.github.io/posologyr/reference/poso_estim_mcmc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the posterior distribution of individual parameters by MCMC — poso_estim_mcmc","text":"dat Dataframe. individual subject dataset following structure NONMEM/rxode2 event records. prior_model posologyr prior population pharmacokinetics model, list six objects. return_model boolean. Returns rxode2 model using estimated ETAs set TRUE. burn_in Number burn-iterations Metropolis-Hastings algorithm. n_iter Total number iterations (following burn-iterations) Markov chain Metropolis-Hastings algorithm. n_chains Number Markov chains nocb boolean. time-varying covariates: next observation carried backward (nocb) interpolation style, similar NONMEM.  FALSE, last observation carried forward (locf) style used. Defaults FALSE. control list parameters controlling Metropolis-Hastings algorithm.","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_estim_mcmc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the posterior distribution of individual parameters by MCMC — poso_estim_mcmc","text":"return_model set FALSE, list one element: dataframe $eta ETAs posterior distribution, estimated Markov Chain Monte Carlo. return_model set TRUE, list dataframe posterior distribution ETA, rxode2 model using estimated distributions ETAs.","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_estim_mcmc.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Estimate the posterior distribution of individual parameters by MCMC — poso_estim_mcmc","text":"Comets  E, Lavenu , Lavielle M. Parameter estimation nonlinear mixed effect models using saemix, R implementation SAEM algorithm. Journal Statistical Software 80, 3 (2017), 1-41.","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_estim_mcmc.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate the posterior distribution of individual parameters by MCMC — poso_estim_mcmc","text":"Emmanuelle Comets, Audrey Lavenu, Marc Lavielle, Cyril Leven","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_estim_mcmc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate the posterior distribution of individual parameters by MCMC — poso_estim_mcmc","text":"","code":"# model mod_run001 <- function() {   ini({     THETA_Cl <- 4.0     THETA_Vc <- 70.0     THETA_Ka <- 1.0     ETA_Cl ~ 0.2     ETA_Vc ~ 0.2     ETA_Ka ~ 0.2     prop.sd <- sqrt(0.05)   })   model({     TVCl <- THETA_Cl     TVVc <- THETA_Vc     TVKa <- THETA_Ka      Cl <- TVCl*exp(ETA_Cl)     Vc <- TVVc*exp(ETA_Vc)     Ka <- TVKa*exp(ETA_Ka)      K20 <- Cl/Vc     Cc <- centr/Vc      d/dt(depot) = -Ka*depot     d/dt(centr) = Ka*depot - K20*centr     Cc ~ prop(prop.sd)   }) } # df_patient01: event table for Patient01, following a 30 minutes intravenous # infusion df_patient01 <- data.frame(ID=1,                         TIME=c(0.0,1.0,14.0),                         DV=c(NA,25.0,5.5),                         AMT=c(2000,0,0),                         EVID=c(1,0,0),                         DUR=c(0.5,NA,NA)) # estimate the posterior distribution of population parameters poso_estim_mcmc(dat=df_patient01,prior_model=mod_run001, n_iter=50,n_chains=2) #>   #>   #>   #>   #> $eta #>          ETA_Cl       ETA_Vc       ETA_Ka #> 1    0.40129734 -0.221078172  0.930425874 #> 2    0.46219626 -0.408861774  0.919479583 #> 3    0.46733199 -0.303257501  0.231586070 #> 4    0.70605689 -0.389072743  0.231586070 #> 5    0.71226862 -0.460682597  0.220690179 #> 6    0.45848168 -0.156826054  0.655923830 #> 7    0.71578116 -0.356073917  0.443066290 #> 8    0.39429314 -0.250227766  0.029447638 #> 9    0.79934008 -0.300123505  0.029447638 #> 10   0.64891523 -0.602467643 -0.257596645 #> 11   0.68534557 -0.393584606  0.471011853 #> 12   0.41497705 -0.469426296  0.200708751 #> 13  -0.28560298 -0.336731063  0.574758130 #> 14   0.69972327 -0.384404332 -0.094838457 #> 15   0.56697115 -0.427648209 -0.154894296 #> 16   0.52583412 -0.193851708  0.029564358 #> 17   0.73647568 -0.145748104  0.240492264 #> 18   0.72223572 -0.174057317  0.765403502 #> 19   0.61003356 -0.349027845  0.290769918 #> 20   0.69904470 -0.047834356  0.422258849 #> 21   0.43859699 -0.612412417 -0.098503394 #> 22   0.69096316 -0.051944909  0.914823122 #> 23   0.54729672  0.005972018  0.337291445 #> 24   0.27761493 -0.237833012  0.271761486 #> 25   0.64326906  0.011240352  0.623424475 #> 26   0.61126409 -0.364932526 -0.290915542 #> 27   0.36850878 -0.351410700  0.388243848 #> 28   0.27661006 -0.581498663 -0.230915305 #> 29  -0.23732985 -0.526465862  0.407033222 #> 30   0.19616325 -0.645638552 -0.494479393 #> 31   0.47330453 -0.034944362  0.470257171 #> 32   0.33639400 -0.515573239 -0.050276895 #> 33   0.78830319 -0.387354276 -0.031447965 #> 34   0.48064119 -0.343826881  0.244052299 #> 35   0.60189998  0.034441740  0.469481783 #> 36   0.77837261 -0.215748962  0.185718893 #> 37   0.75914818 -0.176417195  0.302207225 #> 38   0.68090244 -0.346820687  0.036191074 #> 39   0.54490321 -0.629963624  0.473465262 #> 40   0.49828937 -0.283363262  0.356249587 #> 41   0.54257518 -0.494014971  0.374892911 #> 42   0.62410466 -0.260718197  0.409061732 #> 43   0.15855684 -0.878222982 -0.131889899 #> 44   0.75140183 -0.141857343  0.326987451 #> 45   0.07179731 -0.032165060  0.268610671 #> 46   0.41210387 -0.258656564  0.462389655 #> 47   0.74462060 -0.382517639  0.673070802 #> 48   0.78287745 -0.073199344  0.521618599 #> 49   0.73952962  0.231836592  0.907839064 #> 50   0.53905726 -0.042012025  0.483275077 #> 51   0.00000000  0.000000000  0.000000000 #> 52   0.42534184 -0.403708105  1.056572451 #> 53   0.55618191 -0.264477715  0.046032939 #> 54   0.45520633 -0.679464662 -0.016242431 #> 55   0.44009565 -0.409873762  0.340896578 #> 56   0.57429692 -0.387726602  0.613547280 #> 57   0.54488941 -0.287829000  0.140149483 #> 58   0.48402318 -0.798975605  0.161344579 #> 59   0.56660040 -0.479785996  0.138614462 #> 60   0.61758410 -0.297867848  0.278458135 #> 61   0.73780864  0.023043168  0.551507628 #> 62   0.68002525 -0.319894513  0.234239231 #> 63   0.89503107 -0.037166313  0.600292939 #> 64   0.78780453 -0.263742002  0.175122402 #> 65   0.51360658  0.052946634  0.892954576 #> 66   0.64420451 -0.318996298  0.650988522 #> 67   0.68813422 -0.251065357  0.415733557 #> 68   0.78325406  0.094584973  0.439275927 #> 69   0.48416155 -0.480703520  0.124244451 #> 70   0.48645250 -0.663751410  0.007279203 #> 71   0.42371624 -0.511424673  0.239185177 #> 72   0.79998492 -0.327110133 -0.287008745 #> 73   0.55606168 -0.300343887 -0.103813643 #> 74  -0.18397797 -0.957175830  0.033660836 #> 75   0.33043554 -0.828096146 -0.054257139 #> 76   0.18819311 -0.839629729  0.143333722 #> 77   0.08262417 -0.414383051 -0.225163689 #> 78   0.37230513 -0.375817501  0.666112298 #> 79   0.51699274 -0.934285571 -0.299695700 #> 80   0.08783468 -0.800477076 -0.147109306 #> 81   0.68133590 -0.368815872  0.040840687 #> 82   0.65031284 -0.323466120  0.398707403 #> 83   0.52147855 -0.586734782 -0.127577976 #> 84   0.71718247 -0.171249178  0.372917693 #> 85   0.52524631 -0.377795316  0.640937425 #> 86   0.80589852  0.156365752  0.993932092 #> 87   0.43878864 -0.746336825  0.356184103 #> 88   0.13803478 -0.812773685  0.083051161 #> 89   0.49241332 -0.249973773  0.507466133 #> 90   0.81523875 -0.116686814  0.532386442 #> 91   0.28613945 -0.434134521 -0.055910865 #> 92   0.16400899 -0.611578714 -0.265540778 #> 93   0.49782289 -0.500626081  0.300302428 #> 94   0.69449874 -0.069173401  0.542000876 #> 95   0.44901310 -0.630662806 -0.542320029 #> 96   0.39487244  0.142613375  1.402640547 #> 97   0.64429477 -0.461460431  0.136638918 #> 98   0.57768550 -0.319792195  0.097921632 #> 99   0.56412639 -0.496782084 -0.041628981 #> 100  0.32575969 -0.106694594  0.762696118 #>  #> $model #> ── Solved rxode2 object ── #> ── Parameters ($params): ── #> # A tibble: 100 × 8 #>    sim.id THETA_Cl THETA_Vc THETA_Ka prop.sd ETA_Cl ETA_Vc  ETA_Ka #>     <int>    <dbl>    <dbl>    <dbl>   <dbl>  <dbl>  <dbl>   <dbl> #>  1      1        4       70        1   0.224  0.401 -0.221  0.930  #>  2      2        4       70        1   0.224  0.462 -0.409  0.919  #>  3      3        4       70        1   0.224  0.467 -0.303  0.232  #>  4      4        4       70        1   0.224  0.706 -0.389  0.232  #>  5      5        4       70        1   0.224  0.712 -0.461  0.221  #>  6      6        4       70        1   0.224  0.458 -0.157  0.656  #>  7      7        4       70        1   0.224  0.716 -0.356  0.443  #>  8      8        4       70        1   0.224  0.394 -0.250  0.0294 #>  9      9        4       70        1   0.224  0.799 -0.300  0.0294 #> 10     10        4       70        1   0.224  0.649 -0.602 -0.258  #> # ℹ 90 more rows #> ── Initial Conditions ($inits): ── #> depot centr   AUC  #>     0     0     0  #>  #> Simulation without uncertainty in parameters, omega, or sigma matricies #>  #> ── First part of data (object): ── #> # A tibble: 200 × 14 #>   sim.id  time  TVCl  TVVc  TVKa    Cl    Vc    Ka   K20  rxCc    Cc    depot #>    <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl> #> 1      1     1     4    70     1  5.98  56.1  2.54 0.106 28.4  28.4   3.19e+2 #> 2      1    14     4    70     1  5.98  56.1  2.54 0.106  8.61  8.61  7.99e-9 #> 3      2     1     4    70     1  6.35  46.5  2.51 0.137 33.7  33.7   3.25e+2 #> 4      2    14     4    70     1  6.35  46.5  2.51 0.137  6.96  6.96 -2.81e-9 #> 5      3     1     4    70     1  6.38  51.7  1.26 0.123 22.2  22.2   7.90e+2 #> 6      3    14     4    70     1  6.38  51.7  1.26 0.123  7.85  7.85  6.03e-5 #> # ℹ 194 more rows #> # ℹ 2 more variables: centr <dbl>, AUC <dbl> #>"},{"path":"https://levenc.github.io/posologyr/reference/poso_estim_sir.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the posterior distribution of individual parameters by SIR — poso_estim_sir","title":"Estimate the posterior distribution of individual parameters by SIR — poso_estim_sir","text":"Estimates posterior distribution individual parameters Sequential Importance Resampling (SIR)","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_estim_sir.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the posterior distribution of individual parameters by SIR — poso_estim_sir","text":"","code":"poso_estim_sir(   dat = NULL,   prior_model = NULL,   n_sample = 10000,   n_resample = 1000,   return_model = TRUE,   nocb = FALSE )"},{"path":"https://levenc.github.io/posologyr/reference/poso_estim_sir.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the posterior distribution of individual parameters by SIR — poso_estim_sir","text":"dat Dataframe. individual subject dataset following structure NONMEM/rxode2 event records. prior_model posologyr prior population pharmacokinetics model, list six objects. n_sample Number samples S-step n_resample Number samples R-step return_model boolean. Returns rxode2 model using estimated ETAs set TRUE. nocb boolean. time-varying covariates: next observation carried backward (nocb) interpolation style, similar NONMEM.  FALSE, last observation carried forward (locf) style used. Defaults FALSE.","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_estim_sir.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the posterior distribution of individual parameters by SIR — poso_estim_sir","text":"return_model set FALSE, list one element: dataframe $eta ETAs posterior distribution, estimated Sequential Importance Resampling. return_model set TRUE, list dataframe posterior distribution ETA, rxode2 model using estimated distributions ETAs.","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_estim_sir.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate the posterior distribution of individual parameters by SIR — poso_estim_sir","text":"","code":"# model mod_run001 <- function() {   ini({     THETA_Cl <- 4.0     THETA_Vc <- 70.0     THETA_Ka <- 1.0     ETA_Cl ~ 0.2     ETA_Vc ~ 0.2     ETA_Ka ~ 0.2     prop.sd <- sqrt(0.05)   })   model({     TVCl <- THETA_Cl     TVVc <- THETA_Vc     TVKa <- THETA_Ka      Cl <- TVCl*exp(ETA_Cl)     Vc <- TVVc*exp(ETA_Vc)     Ka <- TVKa*exp(ETA_Ka)      K20 <- Cl/Vc     Cc <- centr/Vc      d/dt(depot) = -Ka*depot     d/dt(centr) = Ka*depot - K20*centr     Cc ~ prop(prop.sd)   }) } # df_patient01: event table for Patient01, following a 30 minutes intravenous # infusion df_patient01 <- data.frame(ID=1,                         TIME=c(0.0,1.0,14.0),                         DV=c(NA,25.0,5.5),                         AMT=c(2000,0,0),                         EVID=c(1,0,0),                         DUR=c(0.5,NA,NA)) # estimate the posterior distribution of population parameters poso_estim_sir(dat=df_patient01,prior_model=mod_run001, n_sample=1e3,n_resample=1e2) #>   #>   #>   #>   #> $eta #>        ETA_Cl      ETA_Vc      ETA_Ka #> 1   0.6504014 -0.46513521  0.43653693 #> 2   0.7998906 -0.03341945  0.16662057 #> 3   0.5739246 -0.05601440  0.71549851 #> 4   0.7089199 -0.17563653  0.37654151 #> 5   0.6224641 -0.44103680  0.11062341 #> 6   0.5756166 -0.54744862 -0.11639086 #> 7   0.2097388 -0.80535811 -0.39535473 #> 8   0.4410245 -0.51368224  0.18837049 #> 9   0.8041649 -0.20072306  0.23829165 #> 10  0.3270809 -0.20504934  0.30219971 #> 11  0.6224641 -0.44103680  0.11062341 #> 12  0.6901039 -0.30108197 -0.14402225 #> 13  0.5756166 -0.54744862 -0.11639086 #> 14  0.6224641 -0.44103680  0.11062341 #> 15  0.7089199 -0.17563653  0.37654151 #> 16  0.7740971 -0.30891172  0.57370521 #> 17  0.6504014 -0.46513521  0.43653693 #> 18  0.6280538 -0.23392662  0.44682453 #> 19  0.7089199 -0.17563653  0.37654151 #> 20  0.3976482 -0.64529135  0.23007851 #> 21  0.3425794 -0.70136613  0.12968425 #> 22  0.5716248 -0.36818884  0.36830570 #> 23  0.2097388 -0.80535811 -0.39535473 #> 24  0.8530497 -0.23492316  0.25581125 #> 25  0.6354166 -0.31357126  0.73866202 #> 26  0.6280538 -0.23392662  0.44682453 #> 27  0.5756166 -0.54744862 -0.11639086 #> 28  0.8530497 -0.23492316  0.25581125 #> 29  0.6354166 -0.31357126  0.73866202 #> 30  0.5480420 -0.78238693 -0.54016365 #> 31  0.6504014 -0.46513521  0.43653693 #> 32  0.6956159 -0.25114851  0.92671472 #> 33  0.3653215 -0.46129773  0.18425818 #> 34  0.4621170 -0.71068616  0.26608879 #> 35  0.2319779 -0.86032500 -0.48170475 #> 36  0.6956159 -0.25114851  0.92671472 #> 37  0.3313779 -0.54665431  0.48542548 #> 38  0.7089199 -0.17563653  0.37654151 #> 39  0.6947877 -0.46349506  0.19509695 #> 40  0.4179024 -0.58931306  0.22519506 #> 41  0.6224641 -0.44103680  0.11062341 #> 42  0.5480420 -0.78238693 -0.54016365 #> 43  0.3653215 -0.46129773  0.18425818 #> 44  0.4796500 -0.65544436 -0.11063743 #> 45  0.4737102 -0.05879126  0.68993753 #> 46  0.6224641 -0.44103680  0.11062341 #> 47  0.8041649 -0.20072306  0.23829165 #> 48  0.6441458 -0.17385456  0.07400396 #> 49  0.5480420 -0.78238693 -0.54016365 #> 50  0.5480420 -0.78238693 -0.54016365 #> 51  0.7089199 -0.17563653  0.37654151 #> 52  0.3769539 -0.82826593  0.23448758 #> 53  0.6834596 -0.07578277  0.01909532 #> 54  0.2527094 -0.21569803  0.25635943 #> 55  0.6431052 -0.37166373 -0.12400846 #> 56  0.4410245 -0.51368224  0.18837049 #> 57  0.7740971 -0.30891172  0.57370521 #> 58  0.2319779 -0.86032500 -0.48170475 #> 59  0.5716248 -0.36818884  0.36830570 #> 60  0.6280538 -0.23392662  0.44682453 #> 61  0.5756166 -0.54744862 -0.11639086 #> 62  0.5716248 -0.36818884  0.36830570 #> 63  0.6014266 -0.46330347  0.48532615 #> 64  0.4621170 -0.71068616  0.26608879 #> 65  0.2708922 -0.52232311 -0.08780402 #> 66  0.5739246 -0.05601440  0.71549851 #> 67  0.5716248 -0.36818884  0.36830570 #> 68  0.3946202 -0.37801414  0.10956928 #> 69  0.6631807  0.37417320  1.32753128 #> 70  0.6224641 -0.44103680  0.11062341 #> 71  0.6177660  0.34344899  0.79709830 #> 72  0.4796500 -0.65544436 -0.11063743 #> 73  0.6280538 -0.23392662  0.44682453 #> 74  0.2324490 -0.80580463 -0.28471752 #> 75  0.3270809 -0.20504934  0.30219971 #> 76  0.6224641 -0.44103680  0.11062341 #> 77  0.7089199 -0.17563653  0.37654151 #> 78  0.6224641 -0.44103680  0.11062341 #> 79  0.5716248 -0.36818884  0.36830570 #> 80  0.5716248 -0.36818884  0.36830570 #> 81  0.5756166 -0.54744862 -0.11639086 #> 82  0.3769539 -0.82826593  0.23448758 #> 83  0.6947877 -0.46349506  0.19509695 #> 84  0.6504014 -0.46513521  0.43653693 #> 85  0.5756166 -0.54744862 -0.11639086 #> 86  0.6224641 -0.44103680  0.11062341 #> 87  0.2944519 -0.31777262  0.32041506 #> 88  0.5065188 -0.43510689  0.59017798 #> 89  0.6354166 -0.31357126  0.73866202 #> 90  0.5065188 -0.43510689  0.59017798 #> 91  0.3951683 -0.42558280  0.21811956 #> 92  0.2486862 -0.80758491 -0.05621151 #> 93  0.7089199 -0.17563653  0.37654151 #> 94  0.4796500 -0.65544436 -0.11063743 #> 95  0.5065188 -0.43510689  0.59017798 #> 96  0.3946202 -0.37801414  0.10956928 #> 97  0.2196114 -0.43600196  0.46038729 #> 98  0.1867108 -0.72833532  0.32243635 #> 99  0.2496268 -0.29416330  0.40590506 #> 100 0.6014266 -0.46330347  0.48532615 #>  #> $model #> ── Solved rxode2 object ── #> ── Parameters ($params): ── #> # A tibble: 100 × 8 #>    sim.id THETA_Cl THETA_Vc THETA_Ka prop.sd ETA_Cl  ETA_Vc ETA_Ka #>     <int>    <dbl>    <dbl>    <dbl>   <dbl>  <dbl>   <dbl>  <dbl> #>  1      1        4       70        1   0.224  0.650 -0.465   0.437 #>  2      2        4       70        1   0.224  0.800 -0.0334  0.167 #>  3      3        4       70        1   0.224  0.574 -0.0560  0.715 #>  4      4        4       70        1   0.224  0.709 -0.176   0.377 #>  5      5        4       70        1   0.224  0.622 -0.441   0.111 #>  6      6        4       70        1   0.224  0.576 -0.547  -0.116 #>  7      7        4       70        1   0.224  0.210 -0.805  -0.395 #>  8      8        4       70        1   0.224  0.441 -0.514   0.188 #>  9      9        4       70        1   0.224  0.804 -0.201   0.238 #> 10     10        4       70        1   0.224  0.327 -0.205   0.302 #> # ℹ 90 more rows #> ── Initial Conditions ($inits): ── #> depot centr   AUC  #>     0     0     0  #>  #> Simulation without uncertainty in parameters, omega, or sigma matricies #>  #> ── First part of data (object): ── #> # A tibble: 200 × 14 #>   sim.id  time  TVCl  TVVc  TVKa    Cl    Vc    Ka   K20  rxCc    Cc   depot #>    <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>   <dbl> #> 1      1     1     4    70     1  7.67  44.0  1.55 0.174 28.5  28.5  6.42e+2 #> 2      1    14     4    70     1  7.67  44.0  1.55 0.174  4.66  4.66 1.18e-6 #> 3      2     1     4    70     1  8.90  67.7  1.18 0.131 16.2  16.2  8.37e+2 #> 4      2    14     4    70     1  8.90  67.7  1.18 0.131  5.45  5.45 1.79e-4 #> 5      3     1     4    70     1  7.10  66.2  2.05 0.107 22.2  22.2  4.50e+2 #> 6      3    14     4    70     1  7.10  66.2  2.05 0.107  7.30  7.30 1.20e-9 #> # ℹ 194 more rows #> # ℹ 2 more variables: centr <dbl>, AUC <dbl> #>"},{"path":"https://levenc.github.io/posologyr/reference/poso_inter_cmin.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the optimal dosing interval to consistently achieve a target trough concentration (Cmin) — poso_inter_cmin","title":"Estimate the optimal dosing interval to consistently achieve a target trough concentration (Cmin) — poso_inter_cmin","text":"Estimates optimal dosing interval consistently achieve target Cmin, given dose, population pharmacokinetic model, set individual parameters, target concentration.","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_inter_cmin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the optimal dosing interval to consistently achieve a target trough concentration (Cmin) — poso_inter_cmin","text":"","code":"poso_inter_cmin(   dat = NULL,   prior_model = NULL,   dose,   target_cmin,   cmt_dose = 1,   endpoint = \"Cc\",   estim_method = \"map\",   nocb = FALSE,   p = NULL,   greater_than = TRUE,   starting_interval = 12,   add_dose = 10,   duration = 0,   indiv_param = NULL )"},{"path":"https://levenc.github.io/posologyr/reference/poso_inter_cmin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the optimal dosing interval to consistently achieve a target trough concentration (Cmin) — poso_inter_cmin","text":"dat Dataframe. individual subject dataset following structure NONMEM/rxode2 event records. prior_model posologyr prior population pharmacokinetics model, list six objects. dose Numeric. dose given. target_cmin Numeric. Target trough concentration (Cmin). cmt_dose Character numeric. compartment dose administered. Must match one compartments prior model. Defaults 1. endpoint Character. endpoint prior model optimised . default \"Cc\", central concentration. estim_method character string. estimation method used individual parameters. default method \"map\" Maximum Posteriori estimation, method \"prior\" simulates prior population model, \"sir\" uses Sequential Importance Resampling algorithm estimate posteriori distribution individual parameters. argument ignored indiv_param provided. nocb boolean. time-varying covariates: next observation carried backward (nocb) interpolation style, similar NONMEM.  FALSE, last observation carried forward (locf) style used. Defaults FALSE. p Numeric. proportion distribution concentrations consider optimization. Mandatory estim_method=sir. greater_than boolean. TRUE: targets dose leading proportion p concentrations greater target_conc. Respectively, lower FALSE. starting_interval Numeric. Starting inter-dose interval optimization algorithm. add_dose Numeric. Additional doses administered inter-dose interval first dose. duration Numeric. Duration infusion, zero-order administrations. indiv_param Optional. set individual parameters : THETA, estimates ETA, covariates.","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_inter_cmin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the optimal dosing interval to consistently achieve a target trough concentration (Cmin) — poso_inter_cmin","text":"list containing following components: interval Numeric. inter-dose interval reach target trough concentration dosing multiple dose regimen. type_of_estimate Character string. type estimate individual parameters. Either point estimate, distribution. conc_estimate vector numeric estimates conc. Either single value (point estimate ETA), distribution. indiv_param data.frame. set individual parameters used determination optimal dose : THETA, estimates ETA, covariates","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_inter_cmin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate the optimal dosing interval to consistently achieve a target trough concentration (Cmin) — poso_inter_cmin","text":"","code":"rxode2::setRxThreads(2L) # limit the number of threads  # model mod_run001 <- function() {   ini({     THETA_Cl <- 4.0     THETA_Vc <- 70.0     THETA_Ka <- 1.0     ETA_Cl ~ 0.2     ETA_Vc ~ 0.2     ETA_Ka ~ 0.2     prop.sd <- sqrt(0.05)   })   model({     TVCl <- THETA_Cl     TVVc <- THETA_Vc     TVKa <- THETA_Ka      Cl <- TVCl*exp(ETA_Cl)     Vc <- TVVc*exp(ETA_Vc)     Ka <- TVKa*exp(ETA_Ka)      K20 <- Cl/Vc     Cc <- centr/Vc      d/dt(depot) = -Ka*depot     d/dt(centr) = Ka*depot - K20*centr     Cc ~ prop(prop.sd)   }) } # df_patient01: event table for Patient01, following a 30 minutes intravenous # infusion df_patient01 <- data.frame(ID=1,                         TIME=c(0.0,1.0,14.0),                         DV=c(NA,25.0,5.5),                         AMT=c(2000,0,0),                         EVID=c(1,0,0),                         DUR=c(0.5,NA,NA)) # estimate the optimal interval to reach a cmin of of 2.5 mg/l # before each administration poso_inter_cmin(dat=df_patient01,prior_model=mod_run001, dose=1500,duration=0.5,target_cmin=2.5) #>   #>   #>   #>   #> $interval #> [1] 17.76029 #>  #> $type_of_estimate #> [1] \"point estimate\" #>  #> $conc_estimate #> [1] 2.500425 #>  #> $indiv_param #>   THETA_Cl THETA_Vc THETA_Ka   prop.sd    ETA_Cl    ETA_Vc    ETA_Ka #> 1        4       70        1 0.2236068 0.6019038 -0.429173 0.1278482 #>"},{"path":"https://levenc.github.io/posologyr/reference/poso_simu_pop.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the prior distribution of population parameters — poso_simu_pop","title":"Estimate the prior distribution of population parameters — poso_simu_pop","text":"Estimates prior distribution population parameters Monte Carlo simulations","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_simu_pop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the prior distribution of population parameters — poso_simu_pop","text":"","code":"poso_simu_pop(   dat = NULL,   prior_model = NULL,   n_simul = 1000,   return_model = TRUE )"},{"path":"https://levenc.github.io/posologyr/reference/poso_simu_pop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the prior distribution of population parameters — poso_simu_pop","text":"dat Dataframe. individual subject dataset following structure NONMEM/rxode2 event records. prior_model posologyr prior population pharmacokinetics model, list six objects. n_simul integer, number simulations run. n_simul =0, ETAs set 0. return_model boolean. Returns rxode2 model using simulated ETAs set TRUE.","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_simu_pop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the prior distribution of population parameters — poso_simu_pop","text":"return_model set FALSE, list one element: dataframe $eta individual values ETA. return_model set TRUE, list dataframe individual values ETA, $model rxode2 model using estimated ETAs, $event data.table used solve returned rxode2 model.","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_simu_pop.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate the prior distribution of population parameters — poso_simu_pop","text":"","code":"# model mod_run001 <- function() {   ini({     THETA_Cl <- 4.0     THETA_Vc <- 70.0     THETA_Ka <- 1.0     ETA_Cl ~ 0.2     ETA_Vc ~ 0.2     ETA_Ka ~ 0.2     prop.sd <- sqrt(0.05)   })   model({     TVCl <- THETA_Cl     TVVc <- THETA_Vc     TVKa <- THETA_Ka      Cl <- TVCl*exp(ETA_Cl)     Vc <- TVVc*exp(ETA_Vc)     Ka <- TVKa*exp(ETA_Ka)      K20 <- Cl/Vc     Cc <- centr/Vc      d/dt(depot) = -Ka*depot     d/dt(centr) = Ka*depot - K20*centr     Cc ~ prop(prop.sd)   }) } # df_patient01: event table for Patient01, following a 30 minutes intravenous # infusion df_patient01 <- data.frame(ID=1,                         TIME=c(0.0,1.0,14.0),                         DV=c(NA,25.0,5.5),                         AMT=c(2000,0,0),                         EVID=c(1,0,0),                         DUR=c(0.5,NA,NA)) # estimate the prior distribution of population parameters poso_simu_pop(dat=df_patient01,prior_model=mod_run001,n_simul=100) #>   #>   #>   #>   #> $eta #>           ETA_Cl       ETA_Vc       ETA_Ka #> 1   -0.626118495  0.114181258 -1.089977423 #> 2   -0.002491555  0.277966827  0.513585283 #> 3   -0.814741627 -0.110607238 -0.109209384 #> 4   -0.126429720 -0.247621892  0.281289321 #> 5    0.923507208 -0.729400635  0.229164299 #> 6   -0.833164068 -0.233451094 -0.023524289 #> 7    0.242835347 -0.408786690  0.209365022 #> 8    0.162316736 -0.583409609  0.329943601 #> 9    0.844565080 -0.043578776 -0.418523660 #> 10  -0.007133196 -0.369751261 -0.676365686 #> 11   0.418307135  0.078928106  0.108979453 #> 12   0.726073134  0.050104954 -0.059925286 #> 13  -0.854217084 -0.124878691 -0.140177303 #> 14   0.477314594  0.031320537 -0.285824640 #> 15  -0.022344982 -0.112466815  0.198919317 #> 16   1.232260201  0.020809466  0.258359350 #> 17   0.052858355 -0.854947394  0.385536795 #> 18  -0.108778777 -0.092164995  0.008576480 #> 19   0.013219971  0.245890352 -1.017015082 #> 20   1.199676043 -0.161543056  0.095415592 #> 21   0.480462085 -0.297436507  0.498174667 #> 22  -0.109968218 -0.526622321 -0.436413663 #> 23   0.476308114  0.058884898  0.218521447 #> 24  -0.760017399 -0.657733272  0.127075897 #> 25   0.598067870  0.105853796  0.589558724 #> 26   0.234299580  0.271345975 -0.049164727 #> 27   0.077002004 -0.040395591  0.860592505 #> 28   0.580658894  0.334869635  0.248751082 #> 29  -0.245188102  0.496646302 -1.168271430 #> 30  -0.069628373  0.194041413 -0.170813730 #> 31   0.189702450  0.475433666  0.468998541 #> 32  -0.017040133  0.217412406  0.748135847 #> 33  -0.158475130  0.423219641  0.588902649 #> 34  -0.132661452 -0.173167175 -0.351256162 #> 35  -0.472587094 -0.355776943 -0.785430249 #> 36  -0.308817936 -0.249787573 -0.240003136 #> 37   0.101574342  0.437578343 -0.093415162 #> 38  -0.625835383  0.115621390 -0.197578722 #> 39   0.254285588  0.951156441  0.190002471 #> 40  -0.753233600  0.111535869  0.479787852 #> 41   0.912033660  0.201001840  0.622438164 #> 42   0.190766359  0.048113024  0.009970508 #> 43   0.269943050 -0.117460907 -0.236246880 #> 44   0.085931834 -0.512596075  0.378425286 #> 45   0.036546129 -0.583666071 -0.422577520 #> 46   0.203187738 -0.382458185 -0.128303442 #> 47   0.400239006  0.030099460 -0.072751070 #> 48  -0.369984355  0.839198826  0.342762477 #> 49   0.438249957  0.591118430 -0.500749906 #> 50   0.230136035 -0.674889964  0.685462828 #> 51   0.191920539  0.054606307 -0.508934617 #> 52  -0.249551952  0.470709543  0.303069339 #> 53   0.017217521 -0.159378512  0.350098525 #> 54   0.359743811 -0.849733032  0.418495455 #> 55  -0.138212034  0.117646994 -0.800777022 #> 56  -0.352520072 -0.506702694  0.162630373 #> 57  -0.127852962  0.231508675 -0.046022156 #> 58  -0.435617165  0.568261928  0.429711796 #> 59   0.343782648  0.463282325 -0.211928742 #> 60  -0.570347095 -0.136677720  0.989133385 #> 61  -0.465848262 -0.512741053 -0.749229147 #> 62   0.682420512  0.247839297  0.891346008 #> 63  -0.068924890  1.146838273  0.474940456 #> 64   0.511028685  0.502596010 -0.177544465 #> 65  -0.368173579 -0.258885074  0.788790589 #> 66   0.059475896  0.168375618  0.509245544 #> 67   0.555109723  0.273735392 -0.192024613 #> 68   0.608416801 -0.031688406 -0.121710828 #> 69  -1.094188573  0.029286516 -0.491268116 #> 70  -0.283165888 -0.922894327  1.184638417 #> 71  -0.515815439 -0.152337889  0.351672035 #> 72  -0.568190736  0.242453072  0.033588380 #> 73   0.249775243  0.185775389 -0.649488201 #> 74   0.420920174 -0.151576730 -0.033797831 #> 75   0.017979951  0.055589127 -0.446512611 #> 76   0.551588806  0.152242459 -0.211398977 #> 77   0.316964005 -0.683771124  0.106179842 #> 78  -0.587108379  0.334081340 -0.698779487 #> 79   0.031776028 -0.286008644 -0.377983025 #> 80   0.301978609  0.515805336 -0.754227850 #> 81  -0.403751120  0.589263704  0.492019811 #> 82   0.538341344 -0.640083750  0.618456538 #> 83   0.001397963 -0.034832047  0.197412704 #> 84   0.057656072 -0.371283104 -0.225213596 #> 85  -0.533812565 -0.336180890  0.651072068 #> 86  -0.370562765  0.129591078 -0.214686445 #> 87  -0.270487910  0.652981124  0.066938642 #> 88  -0.641000683 -0.004607784 -0.094914840 #> 89  -0.405327650 -0.940111168  0.846736541 #> 90  -0.432959036 -0.045885473  0.107313183 #> 91   0.027234813 -0.973841605 -0.052708658 #> 92   0.050219756  0.003526815  0.839752588 #> 93   0.965425280  0.317393983  0.343005395 #> 94  -0.137836338  0.452580985 -0.411012369 #> 95   0.251951230  0.144218670  0.163981760 #> 96   0.505277641 -0.421050740  0.097419956 #> 97   0.632991621 -0.171610636 -0.077853793 #> 98  -0.099167163 -0.451474970  0.214986875 #> 99   0.717512770 -0.677539567 -0.633265146 #> 100  0.392106741  0.279120500  0.944639121 #>  #> $model #> ── Solved rxode2 object ── #> ── Parameters ($params): ── #> # A tibble: 100 × 8 #>    sim.id THETA_Cl THETA_Vc THETA_Ka prop.sd   ETA_Cl  ETA_Vc  ETA_Ka #>     <int>    <dbl>    <dbl>    <dbl>   <dbl>    <dbl>   <dbl>   <dbl> #>  1      1        4       70        1   0.224 -0.626    0.114  -1.09   #>  2      2        4       70        1   0.224 -0.00249  0.278   0.514  #>  3      3        4       70        1   0.224 -0.815   -0.111  -0.109  #>  4      4        4       70        1   0.224 -0.126   -0.248   0.281  #>  5      5        4       70        1   0.224  0.924   -0.729   0.229  #>  6      6        4       70        1   0.224 -0.833   -0.233  -0.0235 #>  7      7        4       70        1   0.224  0.243   -0.409   0.209  #>  8      8        4       70        1   0.224  0.162   -0.583   0.330  #>  9      9        4       70        1   0.224  0.845   -0.0436 -0.419  #> 10     10        4       70        1   0.224 -0.00713 -0.370  -0.676  #> # ℹ 90 more rows #> ── Initial Conditions ($inits): ── #> depot centr   AUC  #>     0     0     0  #>  #> Simulation without uncertainty in parameters, omega, or sigma matricies #>  #> ── First part of data (object): ── #> # A tibble: 15,100 × 14 #>   sim.id  time  TVCl  TVVc  TVKa    Cl    Vc    Ka    K20   rxCc     Cc depot #>    <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>  <dbl>  <dbl> <dbl> #> 1      1   0       4    70     1  2.14  78.5 0.336 0.0273 0      0         0  #> 2      1   0.1     4    70     1  2.14  78.5 0.336 0.0273 0.0847 0.0847  393. #> 3      1   0.2     4    70     1  2.14  78.5 0.336 0.0273 0.335  0.335   774. #> 4      1   0.3     4    70     1  2.14  78.5 0.336 0.0273 0.744  0.744  1141. #> 5      1   0.4     4    70     1  2.14  78.5 0.336 0.0273 1.31   1.31   1497. #> 6      1   0.5     4    70     1  2.14  78.5 0.336 0.0273 2.02   2.02   1841. #> # ℹ 15,094 more rows #> # ℹ 2 more variables: centr <dbl>, AUC <dbl> #>  #> $event #> ── EventTable with 152 records ── #> 1 dosing records (see $get.dosing(); add with add.dosing or et) #> 151 observation times (see $get.sampling(); add with add.sampling or et) #> ── First part of : ── #> # A tibble: 152 × 5 #>       id  time   amt evid          dur        #>    <int> <dbl> <dbl> <evid>        <rate/dur> #>  1     1   0      NA 0:Observation NA         #>  2     1   0    2000 1:Dose (Add)   0.5       #>  3     1   0.1    NA 0:Observation NA         #>  4     1   0.2    NA 0:Observation NA         #>  5     1   0.3    NA 0:Observation NA         #>  6     1   0.4    NA 0:Observation NA         #>  7     1   0.5    NA 0:Observation NA         #>  8     1   0.6    NA 0:Observation NA         #>  9     1   0.7    NA 0:Observation NA         #> 10     1   0.8    NA 0:Observation NA         #> # ℹ 142 more rows #>"},{"path":"https://levenc.github.io/posologyr/reference/poso_time_cmin.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the time required to reach a target trough concentration (Cmin) — poso_time_cmin","title":"Estimate the time required to reach a target trough concentration (Cmin) — poso_time_cmin","text":"Estimates time required reach target trough concentration (Cmin) given population pharmacokinetic model, set individual parameters, dose, target Cmin.","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_time_cmin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the time required to reach a target trough concentration (Cmin) — poso_time_cmin","text":"","code":"poso_time_cmin(   dat = NULL,   prior_model = NULL,   tdm = FALSE,   target_cmin,   dose = NULL,   cmt_dose = 1,   endpoint = \"Cc\",   estim_method = \"map\",   nocb = FALSE,   p = NULL,   greater_than = TRUE,   from = 0.2,   last_time = 72,   add_dose = NULL,   interdose_interval = NULL,   duration = 0,   indiv_param = NULL )"},{"path":"https://levenc.github.io/posologyr/reference/poso_time_cmin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the time required to reach a target trough concentration (Cmin) — poso_time_cmin","text":"dat Dataframe. individual subject dataset following structure NONMEM/rxode2 event records. prior_model posologyr prior population pharmacokinetics model, list six objects. tdm boolean. TRUE: computes predicted time reach target trough concentration (Cmin) following last event dat, using Maximum Posteriori estimation. Setting tdm TRUE causes following occur: simulation  starts time last recorded dose (TDM data) plus ; simulation stops time last recorded dose (TDM data) plus last_time; arguments dose, duration, estim_method, p, greater_than, interdose_interval, add_dose, indiv_param starting_time ignored. target_cmin Numeric. Target trough concentration (Cmin). dose Numeric. Dose administered. argument ignored tdm set TRUE. cmt_dose Character numeric. compartment dose administered. Must match one compartments prior model. Defaults 1. endpoint Character. endpoint prior model optimised . default \"Cc\", central concentration. estim_method character string. estimation method used individual parameters. default method \"map\" Maximum Posteriori estimation, method \"prior\" simulates prior population model, \"sir\" uses Sequential Importance Resampling algorithm estimate posteriori distribution individual parameters. argument ignored indiv_param provided, tdm set TRUE. nocb boolean. time-varying covariates: next observation carried backward (nocb) interpolation style, similar NONMEM. FALSE, last observation carried forward (locf) style used. Defaults FALSE. p Numeric. proportion distribution Cmin consider estimation. Mandatory estim_method=sir. argument ignored tdm set TRUE. greater_than boolean. TRUE: targets time leading proportion p cmins greater target_cmin. Respectively, lower FALSE. argument ignored tdm set TRUE. Numeric. Starting time simulation individual time-concentration profile. default value 0.2. tdm set TRUE simulation starts time last recorded dose plus . last_time Numeric. Ending time simulation individual time-concentration profile. default value 72. tdm set TRUE simulation stops time last recorded dose plus last_time. add_dose Numeric. Additional doses administered inter-dose interval first dose. Optional. argument ignored tdm set TRUE. interdose_interval Numeric. Time inter-dose interval multiple dose regimen. Must provided add_dose used. argument ignored tdm set TRUE. duration Numeric. Duration infusion, zero-order administrations. argument ignored tdm set TRUE. indiv_param Optional. set individual parameters : THETA, estimates ETA, covariates.","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_time_cmin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the time required to reach a target trough concentration (Cmin) — poso_time_cmin","text":"list containing following components: time Numeric. Time needed reach selected Cmin. type_of_estimate Character string. type estimate individual parameters. Either point estimate, distribution. cmin_estimate vector numeric estimates Cmin. Either single value (point estimate ETA), distribution. indiv_param data.frame. set individual parameters used determination time needed reach selected Cmin: THETA, estimates ETA, covariates","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_time_cmin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate the time required to reach a target trough concentration (Cmin) — poso_time_cmin","text":"","code":"rxode2::setRxThreads(2L) # limit the number of threads  # model mod_run001 <- function() {   ini({     THETA_Cl <- 4.0     THETA_Vc <- 70.0     THETA_Ka <- 1.0     ETA_Cl ~ 0.2     ETA_Vc ~ 0.2     ETA_Ka ~ 0.2     prop.sd <- sqrt(0.05)   })   model({     TVCl <- THETA_Cl     TVVc <- THETA_Vc     TVKa <- THETA_Ka      Cl <- TVCl*exp(ETA_Cl)     Vc <- TVVc*exp(ETA_Vc)     Ka <- TVKa*exp(ETA_Ka)      K20 <- Cl/Vc     Cc <- centr/Vc      d/dt(depot) = -Ka*depot     d/dt(centr) = Ka*depot - K20*centr     Cc ~ prop(prop.sd)   }) } # df_patient01: event table for Patient01, following a 30 minutes intravenous # infusion df_patient01 <- data.frame(ID=1,                         TIME=c(0.0,1.0,14.0),                         DV=c(NA,25.0,5.5),                         AMT=c(2000,0,0),                         EVID=c(1,0,0),                         DUR=c(0.5,NA,NA)) # predict the time needed to reach a concentration of 2.5 mg/l # after the administration of a 2500 mg dose over a 30 minutes # infusion poso_time_cmin(dat=df_patient01,prior_model=mod_run001, dose=2500,duration=0.5,from=0.5,target_cmin=2.5) #>   #>   #>   #>   #> $time #> [1] 20.5 #>  #> $type_of_estimate #> [1] \"point estimate\" #>  #> $cmin_estimate #> [1] 2.489933 #>  #> $indiv_param #>   THETA_Cl THETA_Vc THETA_Ka   prop.sd    ETA_Cl     ETA_Vc    ETA_Ka #> 1        4       70        1 0.2236068 0.6019036 -0.4291735 0.1278478 #>"},{"path":"https://levenc.github.io/posologyr/reference/posologyr_error_lines.html","id":null,"dir":"Reference","previous_headings":"","what":"This creates the posologyr error lines from a rxui model — posologyr_error_lines","title":"This creates the posologyr error lines from a rxui model — posologyr_error_lines","text":"creates posologyr error lines rxui model","code":""},{"path":"https://levenc.github.io/posologyr/reference/posologyr_error_lines.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"This creates the posologyr error lines from a rxui model — posologyr_error_lines","text":"","code":"posologyr_error_lines(line)  # S3 method for class 'norm' posologyr_error_lines(line)  # S3 method for class 't' posologyr_error_lines(line)  # Default S3 method posologyr_error_lines(line)  # S3 method for class 'rxUi' posologyr_error_lines(line)"},{"path":"https://levenc.github.io/posologyr/reference/posologyr_error_lines.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"This creates the posologyr error lines from a rxui model — posologyr_error_lines","text":"line line parse","code":""},{"path":"https://levenc.github.io/posologyr/reference/posologyr_error_lines.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"This creates the posologyr error lines from a rxui model — posologyr_error_lines","text":"error lines posology","code":""},{"path":"https://levenc.github.io/posologyr/reference/posologyr_error_lines.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"This creates the posologyr error lines from a rxui model — posologyr_error_lines","text":"Matthew L. Fidler","code":""},{"path":[]},{"path":"https://levenc.github.io/posologyr/news/index.html","id":"additional-feature-1-2-4-9000","dir":"Changelog","previous_headings":"","what":"Additional feature","title":"posologyr v1.2.4.9000","text":"route administration (.e. compartment drug administered) can now specified poso_time_cmin(), poso_dose_conc(), poso_dose_auc() poso_inter_cmin(). poso_simu_pop() provides rxode2 model using simulated ETA input dataset, interpolation covariates, make plotting easier","code":""},{"path":"https://levenc.github.io/posologyr/news/index.html","id":"documentation-1-2-4-9000","dir":"Changelog","previous_headings":"","what":"Documentation","title":"posologyr v1.2.4.9000","text":"README illustrates simple example dose adaptation vignette(\"route_of_administration\") shows select route administration optimal dosing vignette(\"population_models\") describes structure prior population models written model functions can parsed rxode2 used posologyr vignette(\"posologyr_user_defined_models\") renamed vignette(\"classic_posologyr_models\") Examples use rxode2 model functions","code":""},{"path":"https://levenc.github.io/posologyr/news/index.html","id":"bug-fix-1-2-4-9000","dir":"Changelog","previous_headings":"","what":"Bug fix","title":"posologyr v1.2.4.9000","text":"Fix bug poso_estim_map(), poso_estim_sir() poso_simu_pop() failed models featuring single parameter IIV.","code":""},{"path":"https://levenc.github.io/posologyr/news/index.html","id":"posologyr-v124","dir":"Changelog","previous_headings":"","what":"posologyr v1.2.4","title":"posologyr v1.2.4","text":"CRAN release: 2024-02-09 Add ability use rxode2 ui models poso_* functions. model parsed rxode2() package model$posologyr gives list needed poso_* functions","code":""},{"path":[]},{"path":"https://levenc.github.io/posologyr/news/index.html","id":"bug-fix-1-2-3","dir":"Changelog","previous_headings":"","what":"Bug fix","title":"posologyr v1.2.3","text":"Fix bug poso_dose_conc(), poso_dose_auc() poso_inter_cmin() returned estimate target value optimized always equal zero.","code":""},{"path":"https://levenc.github.io/posologyr/news/index.html","id":"documentation-1-2-3","dir":"Changelog","previous_headings":"","what":"Documentation","title":"posologyr v1.2.3","text":"documentation poso_time_cmin(), poso_dose_conc(), poso_dose_auc() now explicitly states consequences setting tdm TRUE: parameters required, parameters ignored, parameters behave differently. functions poso_time_cmin(), poso_dose_conc(), poso_dose_auc() now return warning input parameters ignored. Fix incorrect information regarding duration AUC documentation poso_dose_auc()","code":""},{"path":"https://levenc.github.io/posologyr/news/index.html","id":"posologyr-v122","dir":"Changelog","previous_headings":"","what":"posologyr v1.2.2","title":"posologyr v1.2.2","text":"CRAN release: 2023-06-12 Relax requirements NONMEM comparison test time-varying covariates account computational differences observed alternative BLAS ATLAS CRAN.","code":""},{"path":"https://levenc.github.io/posologyr/news/index.html","id":"posologyr-v121","dir":"Changelog","previous_headings":"","what":"posologyr v1.2.1","title":"posologyr v1.2.1","text":"CRAN release: 2023-06-05 Add reference Kang et al. (2012) doi:10.4196/kjpp.2012.16.2.97 DESCRIPTION (requested CRAN) Fix messages console internal function posologyr() (requested CRAN) Fix assignment parent environment dose optim functions, using parent.frame() (requested CRAN)","code":""},{"path":[]},{"path":"https://levenc.github.io/posologyr/news/index.html","id":"additional-features-1-2-0","dir":"Changelog","previous_headings":"","what":"Additional features","title":"posologyr v1.2.0","text":"poso_estim_map(), poso_estim_sir() poso_estim_mcmc() can now estimate individual PK profiles multiple endpoints models (eg. PK-PD, parent-metabolite, blood-CSF…), using different residual error model endpoint. poso_time_cmin(), poso_dose_conc(), poso_dose_auc() poso_inter_cmin() now allow select end point interest want optimise, provided defined model.","code":""},{"path":"https://levenc.github.io/posologyr/news/index.html","id":"documentation-1-2-0","dir":"Changelog","previous_headings":"","what":"Documentation","title":"posologyr v1.2.0","text":"vignette(\"a_priori_dosing\") illustrates priori dose selection vignette(\"a_posteriori_dosing\") illustrates posteriori dose selection, using TDM data vignette(\"auc_based_dosing\") shows select optimal dose given target AUC using data TDM vignette(\"multiple_endpoints\") introduces new multiple endpoints feature","code":""},{"path":"https://levenc.github.io/posologyr/news/index.html","id":"internal-changes-1-2-0","dir":"Changelog","previous_headings":"","what":"Internal changes","title":"posologyr v1.2.0","text":"description package updated","code":""},{"path":[]},{"path":"https://levenc.github.io/posologyr/news/index.html","id":"additional-features-1-1-0","dir":"Changelog","previous_headings":"","what":"Additional features","title":"posologyr v1.1.0","text":"poso_time_cmin() can now estimate time needed reach selected trough concentration (Cmin) using data TDM directly poso_dose_conc() can now estimate optimal dose reach target concentration following events TDM poso_dose_auc() can now estimate optimal dose reach target auc following events TDM","code":""},{"path":[]},{"path":"https://levenc.github.io/posologyr/news/index.html","id":"breaking-changes-1-0-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"posologyr v1.0.0","text":"posologyr() now internal function, exported functions take patient data prior model input parameters adaptive MAP forecasting option removed","code":""},{"path":"https://levenc.github.io/posologyr/news/index.html","id":"additional-features-1-0-0","dir":"Changelog","previous_headings":"","what":"Additional features","title":"posologyr v1.0.0","text":"poso_estim_map() provides rxode2 model using MAP-EBE input dataset, interpolation covariates, make plotting easier","code":""},{"path":"https://levenc.github.io/posologyr/news/index.html","id":"internal-changes-1-0-0","dir":"Changelog","previous_headings":"","what":"Internal changes","title":"posologyr v1.0.0","text":"RxODE import updated rxode2 tests updated take account internalization posologyr() function","code":""},{"path":"https://levenc.github.io/posologyr/news/index.html","id":"bug-fixes-1-0-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"posologyr v1.0.0","text":"poso_time_cmin(), poso_dose_auc(), poso_dose_conc(), poso_inter_cmin() longer fail models IOV","code":""},{"path":"https://levenc.github.io/posologyr/news/index.html","id":"posologyr-v020","dir":"Changelog","previous_headings":"","what":"posologyr v0.2.0","title":"posologyr v0.2.0","text":"poso_estim_sir() estimates posterior distribution individual parameters Sequential Importance Resampling (SIR). roughly 25 times faster poso_estim_mcmc() 1000 samples. poso_estim_map() allows estimation individual parameters adaptive MAP forecasting (cf. doi: 10.1007/s11095-020-02908-7) adapt=TRUE. poso_simu_pop(), poso_estim_map(), poso_estim_sir() now support models inter-individual (IIV) inter-occasion variability (IOV). MASS:mvrnorm replaced mvtnorm::rmvnorm multivariate normal distributions. Input validation added exported functions. poso_estim_map() now uses method=“L-BFGS-B” optim better convergence algorithm. poso_inter_cmin() now uses method=“L-BFGS-B” optim better convergence algorithm. poso_dose_conc() new name poso_dose_ctime(). Issues #5 #6 fixed: poso_time_cmin(), poso_dose_auc(), poso_dose_conc(), poso_inter_cmin() now work prior posterior distributions ETA, point estimates (MAP). new nocb parameter added posologyr(). interpolation method time-varying covariates can either last observation carried forward (locf, RxODE default), next observation carried backward (nocb, NONMEM default). vignette(\"uncertainty_estimates\") removed. built-models removed.","code":""},{"path":"https://levenc.github.io/posologyr/news/index.html","id":"posologyr-v011","dir":"Changelog","previous_headings":"","what":"posologyr v0.1.1","title":"posologyr v0.1.1","text":"poso_time_cmin(), poso_dose_ctime(), poso_dose_auc() now work multiple dose regimen. poso_inter_cmin() allows optimization inter-dose interval multiple dose regimen. vignette(\"case_study_vancomycin\") illustrates AUC-based optimal dosing, multiple dose regimen, continuous intravenous infusion.","code":""},{"path":"https://levenc.github.io/posologyr/news/index.html","id":"posologyr-v010","dir":"Changelog","previous_headings":"","what":"posologyr v0.1.0","title":"posologyr v0.1.0","text":"First public release.","code":""}]
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END TERMS CONDITIONS","code":""},{"path":"https://levenc.github.io/posologyr/articles/a_posteriori_dosing.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"A posteriori dose selection","text":"Dosage individualization critical care patient treated amikacin suspected ventilator-associated pneumonia, using population pharmacokinetic (ppk) model Burdet et al. 2015, using data therapeutic drug monitoring (TDM).","code":"mod_amikacin_Burdet2015 <- function() {     ini({       THETA_Cl=4.3       THETA_Vc=15.9       THETA_Vp=21.4       THETA_Q=12.1       ETA_Cl + ETA_Vc + ETA_Vp + ETA_Q ~         c(0.1,           0.01     ,   0.05 ,           0.01     ,   0.02 ,   0.2  ,           -0.06    ,   0.004,   0.003,    0.08)       add_sd <- 0.2       prop_sd <- 0.1     })     model({       TVCl  = THETA_Cl*(CLCREAT4H/82)^0.7       TVVc  = THETA_Vc*(TBW/78)^0.9*(PoverF/169)^0.4       TVVp  = THETA_Vp       TVQ   = THETA_Q       Cl    = TVCl*exp(ETA_Cl)       Vc    = TVVc*exp(ETA_Vc)       Vp    = TVVp*exp(ETA_Vp)       Q     = TVQ *exp(ETA_Q)       ke    = Cl/Vc       k12   = Q/Vc       k21   = Q/Vp       Cp    = centr/Vc       d/dt(centr)  = - ke*centr - k12*centr + k21*periph       d/dt(periph) =            + k12*centr - k21*periph        Cp ~ add(add_sd) + prop(prop_sd) + combined1()     })   }"},{"path":[]},{"path":"https://levenc.github.io/posologyr/articles/a_posteriori_dosing.html","id":"patient-record-with-tdm-data","dir":"Articles","previous_headings":"A posteriori dose selection","what":"Patient record with TDM data","title":"A posteriori dose selection","text":"first administration, dosage selection can refined using results TDM. See vignette(\"patient_data_input\") details regarding patient record. concentration measured 30 min 30 min infusion meet target peak concentration; < 60 mg/L.","code":"df_patientA <- data.frame(ID=1,TIME=c(0,1,6),                               DV=c(NA,58,14),                               EVID=c(1,0,0),                               AMT=c(2000,0,0),                               DUR=c(0.5,NA,NA),                               CLCREAT4H=50,TBW=62,PoverF=169) df_patientA #>   ID TIME DV EVID  AMT DUR CLCREAT4H TBW PoverF #> 1  1    0 NA    1 2000 0.5        50  62    169 #> 2  1    1 58    0    0  NA        50  62    169 #> 3  1    6 14    0    0  NA        50  62    169"},{"path":"https://levenc.github.io/posologyr/articles/a_posteriori_dosing.html","id":"estimate-the-map-individual-parameters","dir":"Articles","previous_headings":"A posteriori dose selection","what":"Estimate the MAP individual parameters","title":"A posteriori dose selection","text":"maximum posteriori (MAP) individual parameters estimated.","code":"patA_map <- poso_estim_map(dat=df_patientA,                            prior_model=mod_amikacin_Burdet2015)"},{"path":"https://levenc.github.io/posologyr/articles/a_posteriori_dosing.html","id":"plot-the-individual-pharmacokinetic-profile","dir":"Articles","previous_headings":"A posteriori dose selection","what":"Plot the individual pharmacokinetic profile","title":"A posteriori dose selection","text":"individual pharmacokinetic profile can plotted using rxode2 model provided poso_estim_map() function.","code":"plot(patA_map$model,Cc)"},{"path":"https://levenc.github.io/posologyr/articles/a_posteriori_dosing.html","id":"time-required-to-reach-the-target-cmin-following-the-first-administration","dir":"Articles","previous_headings":"A posteriori dose selection","what":"Time required to reach the target Cmin following the first administration","title":"A posteriori dose selection","text":"MAP estimates individual parameters, prediction time needed reaching target Cmin can updated. next dose (needed) can administered 33.9 hours following first infusion.","code":"poso_time_cmin(dat=df_patientA,                prior_model=mod_amikacin_Burdet2015,                tdm = TRUE,                target_cmin = 2.5) #> $time #> [1] 33.9 #>  #> $type_of_estimate #> [1] \"point estimate\" #>  #> $cmin_estimate #> [1] 2.487865 #>  #> $indiv_param #>   THETA_Cl THETA_Vc THETA_Vp THETA_Q add_sd prop_sd    ETA_Cl    ETA_Vc #> 3      4.3     15.9     21.4    12.1    0.2     0.1 0.4499479 0.2730561 #>      ETA_Vp    ETA_Q CLCREAT4H TBW PoverF #> 3 0.7061648 -0.13884        50  62    169"},{"path":"https://levenc.github.io/posologyr/articles/a_posteriori_dosing.html","id":"optimal-dose-selection-a-posteriori","dir":"Articles","previous_headings":"A posteriori dose selection","what":"Optimal dose selection a posteriori","title":"A posteriori dose selection","text":"optimal dose achieve peak concentration 80 mg/l can determined using MAP estimates. next dose 2450 mg.","code":"map_dose <- poso_dose_conc(dat=df_patientA,                            prior_model=mod_amikacin_Burdet2015,                            tdm=TRUE,                            time_c = 35,               #target concentration at t = 35 h                            time_dose = 34,            #dosing at t = 34 h                            duration = 0.5,                            target_conc = 80) map_dose #> $dose #> [1] 2447.917 #>  #> $type_of_estimate #> [1] \"point estimate\" #>  #> $conc_estimate #> [1] 80 #>  #> $indiv_param #>   THETA_Cl THETA_Vc THETA_Vp THETA_Q add_sd prop_sd    ETA_Cl    ETA_Vc #> 3      4.3     15.9     21.4    12.1    0.2     0.1 0.4499608 0.2730596 #>      ETA_Vp      ETA_Q CLCREAT4H TBW PoverF #> 3 0.7061496 -0.1388505        50  62    169"},{"path":"https://levenc.github.io/posologyr/articles/a_posteriori_dosing.html","id":"interdose-interval-selection-a-posteriori","dir":"Articles","previous_headings":"A posteriori dose selection","what":"Interdose interval selection a posteriori","title":"A posteriori dose selection","text":"optimal inter-dose interval reach Cmin 2.5 mg/L dosing can determined using MAP estimates. interval doses less 38.6 hours allow adequate elimination amikacin infusion.","code":"map_interval <- poso_inter_cmin(dat=df_patientA,                                 prior_model=mod_amikacin_Burdet2015,                                 dose = map_dose$dose,                                 duration = 0.5,                                 target_cmin = 2.5) map_interval #> $interval #> [1] 38.57781 #>  #> $type_of_estimate #> [1] \"point estimate\" #>  #> $conc_estimate #> [1] 2.500173 #>  #> $indiv_param #>   THETA_Cl THETA_Vc THETA_Vp THETA_Q add_sd prop_sd   ETA_Cl    ETA_Vc #> 1      4.3     15.9     21.4    12.1    0.2     0.1 0.449952 0.2730587 #>      ETA_Vp      ETA_Q CLCREAT4H TBW PoverF #> 1 0.7061588 -0.1388432        50  62    169"},{"path":"https://levenc.github.io/posologyr/articles/a_priori_dosing.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"A priori dose selection","text":"First dose selection critical care patient treated amikacin suspected ventilator-associated pneumonia. Population pharmacokinetic (ppk) model form Burdet et al. 2015.","code":"mod_amikacin_Burdet2015 <- function() {     ini({       THETA_Cl=4.3       THETA_Vc=15.9       THETA_Vp=21.4       THETA_Q=12.1       ETA_Cl + ETA_Vc + ETA_Vp + ETA_Q ~         c(0.1,           0.01     ,   0.05 ,           0.01     ,   0.02 ,   0.2  ,           -0.06    ,   0.004,   0.003,    0.08)       add_sd <- 0.2       prop_sd <- 0.1     })     model({       TVCl  = THETA_Cl*(CLCREAT4H/82)^0.7       TVVc  = THETA_Vc*(TBW/78)^0.9*(PoverF/169)^0.4       TVVp  = THETA_Vp       TVQ   = THETA_Q       Cl    = TVCl*exp(ETA_Cl)       Vc    = TVVc*exp(ETA_Vc)       Vp    = TVVp*exp(ETA_Vp)       Q     = TVQ *exp(ETA_Q)       ke    = Cl/Vc       k12   = Q/Vc       k21   = Q/Vp       Cp    = centr/Vc       d/dt(centr)  = - ke*centr - k12*centr + k21*periph       d/dt(periph) =            + k12*centr - k21*periph        Cp ~ add(add_sd) + prop(prop_sd) + combined1()     })   }"},{"path":[]},{"path":"https://levenc.github.io/posologyr/articles/a_priori_dosing.html","id":"patient-record","dir":"Articles","previous_headings":"A priori dose selection","what":"Patient record","title":"A priori dose selection","text":"first administration, concentration information available. patient record contains information required fill covariates model: CLCREAT4H: 4-h creatinine clearance ml/min TBW: Total body weight kg PoverF: PaO2/FIO2 ratio mmHg","code":"df_patientA <- data.frame(ID=1,TIME=0,                                 DV=0,                                 EVID=0,                                 AMT=0,                                 CLCREAT4H=50,TBW=62,PoverF=169) df_patientA #>   ID TIME DV EVID AMT CLCREAT4H TBW PoverF #> 1  1    0  0    0   0        50  62    169"},{"path":"https://levenc.github.io/posologyr/articles/a_priori_dosing.html","id":"optimal-dose-selection","dir":"Articles","previous_headings":"A priori dose selection","what":"Optimal dose selection","title":"A priori dose selection","text":"absence measured concentrations, optimal dose mg achieve concentration 80 mg/l one hour start 30-minute infusion determined typical profile ppk model.","code":"prior_dose <- poso_dose_conc(dat=df_patientA,                              prior_model=mod_amikacin_Burdet2015,                              time_c = 1,                        #30 min after a                                duration = 0.5,                    #30 min infusion                              target_conc = 80) prior_dose #> $dose #> [1] 2087.669 #>  #> $type_of_estimate #> [1] \"point estimate\" #>  #> $conc_estimate #> [1] 80 #>  #> $indiv_param #>   THETA_Cl THETA_Vc THETA_Vp THETA_Q add_sd prop_sd       ETA_Cl       ETA_Vc #> 1      4.3     15.9     21.4    12.1    0.2     0.1 2.025724e-07 6.573817e-08 #>         ETA_Vp         ETA_Q CLCREAT4H TBW PoverF #> 1 2.011353e-07 -1.163665e-07        50  62    169"},{"path":"https://levenc.github.io/posologyr/articles/a_priori_dosing.html","id":"time-required-to-reach-the-target-cmin","dir":"Articles","previous_headings":"A priori dose selection","what":"Time required to reach the target Cmin","title":"A priori dose selection","text":"Following dose, time hours required reach target Cmin concentration 2.5 mg/l can estimated.","code":"poso_time_cmin(dat=df_patientA,                prior_model=mod_amikacin_Burdet2015,                dose = prior_dose$dose,                duration = 0.5,                                  #30 min infusion                target_cmin = 2.5) #> $time #> [1] 37.5 #>  #> $type_of_estimate #> [1] \"point estimate\" #>  #> $cmin_estimate #> [1] 2.49637 #>  #> $indiv_param #>   THETA_Cl THETA_Vc THETA_Vp THETA_Q add_sd prop_sd        ETA_Cl       ETA_Vc #> 1      4.3     15.9     21.4    12.1    0.2     0.1 -9.803026e-07 3.556777e-07 #>          ETA_Vp        ETA_Q CLCREAT4H TBW PoverF #> 1 -3.567767e-07 9.847164e-07        50  62    169"},{"path":"https://levenc.github.io/posologyr/articles/a_priori_dosing.html","id":"plotting-the-selected-dosage","dir":"Articles","previous_headings":"A priori dose selection","what":"Plotting the selected dosage","title":"A priori dose selection","text":"selected dose can simulated plotted. setting n_simul = 0, poso_simu_pop() function produces compiled rxode2 model without inter-individual variability, using typical population parameter values individual covariates patient record. Observations 30-minutes infusion optimal dose added rxode2 model updating rxode2 event table. Plotting simulated scenario.  resulting plot can augmented ggplot2. example, adding horizontal ribbon showing 60-80 mg/l target interval 1 h peak concentration, vertical dashed line marking 1 hour.  typical patient (.e. PK profile typical model population), selected dose meets peak concentration target.","code":"# generate a model using the individual covariates  simu_patA      <- poso_simu_pop(dat=df_patientA,                                 prior_model=mod_amikacin_Burdet2015,                                 n_simul = 0) simu_patA$model$time <- seq(0,20,b=0.1) simu_patA$model$add.dosing(dose=prior_dose$dose,rate=prior_dose$dose/0.5) plot(simu_patA$model,Cc) plot(simu_patA$model,Cc) +    ggplot2::ylab(\"Central concentration\") +   ggplot2::geom_vline(xintercept=1, linetype=\"dashed\") +   ggplot2::geom_ribbon(ggplot2::aes(ymin=60, ymax=80),                        fill=\"seagreen\",show.legend = FALSE, alpha=0.15)"},{"path":"https://levenc.github.io/posologyr/articles/auc_based_dosing.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"AUC-based dose selection","text":"AUC-based dosage adjustment patient treated vancomycin methicillin-resistant Staphylococcus aureus blood stream infection, using population pharmacokinetic (ppk) model Goti et al. 2018, using data therapeutic drug monitoring (TDM).","code":"mod_vancomycin_Goti2018 <- function() {     ini({       THETA_Cl <- 4.5       THETA_Vc <- 58.4       THETA_Vp <- 38.4       THETA_Q <- 6.5       ETA_Cl ~ 0.147       ETA_Vc ~ 0.510       ETA_Vp ~ 0.282       add.sd <- 3.4       prop.sd <- 0.227     })     model({       TVCl  = THETA_Cl*(CLCREAT/120)^0.8*(0.7^DIAL);       TVVc  = THETA_Vc*(WT/70)          *(0.5^DIAL);       TVVp  = THETA_Vp;       TVQ   = THETA_Q;       Cl    = TVCl*exp(ETA_Cl);       Vc    = TVVc*exp(ETA_Vc);       Vp    = TVVp*exp(ETA_Vp);       Q     = TVQ;       ke    = Cl/Vc;       k12   = Q/Vc;       k21   = Q/Vp;       Cc    = centr/Vc;       d/dt(centr)  = - ke*centr - k12*centr + k21*periph;       d/dt(periph) =            + k12*centr - k21*periph;       d/dt(AUC) <- Cc       Cc ~ add(add.sd) + prop(prop.sd) + combined1()     })   }"},{"path":[]},{"path":"https://levenc.github.io/posologyr/articles/auc_based_dosing.html","id":"patient-record-with-tdm-data","dir":"Articles","previous_headings":"Discontinuous intravenous infusion","what":"Patient record with TDM data","title":"AUC-based dose selection","text":"dosage selection can informed using results TDM. See vignette(\"patient_data_input\") details regarding patient records.","code":"df_patientB <- data.frame(ID=1,TIME=c(0.0,13.0,24.2,48),                           DV=c(NA,12,NA,9.5),                           AMT=c(2000,0,1000,0),                           DUR=c(2,NA,2,NA),                           EVID=c(1,0,1,0),                           CLCREAT=65,WT=70,DIAL=0) df_patientB #>   ID TIME   DV  AMT DUR EVID CLCREAT WT DIAL #> 1  1  0.0   NA 2000   2    1      65 70    0 #> 2  1 13.0 12.0    0  NA    0      65 70    0 #> 3  1 24.2   NA 1000   2    1      65 70    0 #> 4  1 48.0  9.5    0  NA    0      65 70    0"},{"path":"https://levenc.github.io/posologyr/articles/auc_based_dosing.html","id":"estimate-the-map-individual-parameters","dir":"Articles","previous_headings":"Discontinuous intravenous infusion","what":"Estimate the MAP individual parameters","title":"AUC-based dose selection","text":"","code":"patB_map <- poso_estim_map(dat=df_patientB,                            prior_model=mod_vancomycin_Goti2018)"},{"path":"https://levenc.github.io/posologyr/articles/auc_based_dosing.html","id":"plot-the-individual-pharmacokinetic-profile","dir":"Articles","previous_headings":"Discontinuous intravenous infusion","what":"Plot the individual pharmacokinetic profile","title":"AUC-based dose selection","text":"individual pharmacokinetic profile can plotted using rxode2 model provided poso_estim_map() function.  Using ggplot2 observed data points can added plot  MAP profile matches observations.","code":"plot(patB_map$model,Cc) #Get the observations from the patient record indiv_obs             <- df_patientB[,c(\"DV\",\"TIME\")] names(indiv_obs)      <- c(\"value\",\"time\")  #Overlay the MAP profile and the observations plot(patB_map$model,Cc) +   ggplot2::ylab(\"Central concentration\") +   ggplot2::geom_point(data=indiv_obs, size= 3, na.rm=TRUE)"},{"path":"https://levenc.github.io/posologyr/articles/auc_based_dosing.html","id":"get-the-auc24-from-the-map-model","dir":"Articles","previous_headings":"Discontinuous intravenous infusion","what":"Get the AUC24 from the MAP model","title":"AUC-based dose selection","text":"Considering MIC 1 mg/L, target AUC 24 hours (AUC24) 400 mg.h/L. AUC can retrieved rxode2 model using usual R data.frame syntax. current dosage meet target AUC.","code":"#AUC 0_24 AUC_map_first_dose <- patB_map$model$AUC[which(patB_map$model$time == 24)] AUC_map_first_dose #> [1] 337.8965  #AUC 24_48 AUC_map_second_dose <- patB_map$model$AUC[which(patB_map$model$time == 48)] - AUC_map_first_dose AUC_map_second_dose #> [1] 325.3072"},{"path":"https://levenc.github.io/posologyr/articles/auc_based_dosing.html","id":"optimal-dose-selection-a-posteriori","dir":"Articles","previous_headings":"Discontinuous intravenous infusion","what":"Optimal dose selection a posteriori","title":"AUC-based dose selection","text":"next dose needed achieve AUC24 400 mg.h/L can estimated using TDM data. optimal dose estimated next infusion 1411 mg.","code":"poso_dose_auc(dat=df_patientB,               prior_model=mod_vancomycin_Goti2018,               tdm=TRUE,               time_auc=24,            #AUC24               time_dose = 48,         #48 h: immediately following the last observation               duration=2,             #infused over 2 h               target_auc=400) #> $dose #> [1] 1411.593 #>  #> $type_of_estimate #> [1] \"point estimate\" #>  #> $auc_estimate #> [1] 400 #>  #> $indiv_param #>   THETA_Cl THETA_Vc THETA_Vp THETA_Q add.sd prop.sd     ETA_Cl     ETA_Vc #> 4      4.5     58.4     38.4     6.5    3.4   0.227 0.08208203 0.06122374 #>       ETA_Vp CLCREAT DIAL WT #> 4 0.06285943      65    0 70"},{"path":"https://levenc.github.io/posologyr/articles/auc_based_dosing.html","id":"optimal-maintenance-dose-selection-a-posteriori","dir":"Articles","previous_headings":"Discontinuous intravenous infusion","what":"Optimal maintenance dose selection a posteriori","title":"AUC-based dose selection","text":"maintenance dose needed reliably achieve AUC24 400 mg.h/L can estimated simulating multiple dose regimen enough administrations (e.g. 11 consecutive administrations, add_dose=10) approximate steady-state. optimal maintenance dose 1200 mg.","code":"poso_dose_auc(dat=df_patientB,               prior_model=mod_vancomycin_Goti2018,               time_auc=24,               starting_time=24*9,               interdose_interval=24,               add_dose=10,               duration=2,               target_auc=400) #> $dose #> [1] 1198.266 #>  #> $type_of_estimate #> [1] \"point estimate\" #>  #> $auc_estimate #> [1] 400 #>  #> $indiv_param #>   THETA_Cl THETA_Vc THETA_Vp THETA_Q add.sd prop.sd     ETA_Cl     ETA_Vc #> 1      4.5     58.4     38.4     6.5    3.4   0.227 0.08208199 0.06122337 #>      ETA_Vp CLCREAT DIAL WT #> 1 0.0628591      65    0 70"},{"path":"https://levenc.github.io/posologyr/articles/auc_based_dosing.html","id":"continuous-intravenous-infusion","dir":"Articles","previous_headings":"","what":"Continuous intravenous infusion","title":"AUC-based dose selection","text":"maintenance dose continuous intravenous infusion can easily determined setting duration infusion equal interdose_interval. optimal maintenance dose also 1200 mg / 24 h continuous intravenous infusion.","code":"poso_dose_auc(dat=df_patientB,               prior_model=mod_vancomycin_Goti2018,               time_auc=24,               starting_time=24*9,               interdose_interval=24,               add_dose=10,               duration=24,               target_auc=400) #> $dose #> [1] 1198.923 #>  #> $type_of_estimate #> [1] \"point estimate\" #>  #> $auc_estimate #> [1] 400 #>  #> $indiv_param #>   THETA_Cl THETA_Vc THETA_Vp THETA_Q add.sd prop.sd     ETA_Cl     ETA_Vc #> 1      4.5     58.4     38.4     6.5    3.4   0.227 0.08208202 0.06122453 #>       ETA_Vp CLCREAT DIAL WT #> 1 0.06285936      65    0 70"},{"path":"https://levenc.github.io/posologyr/articles/classic_posologyr_models.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Classic posologyr models","text":"Originally, posologyr models R lists; posologyr still uses format internally, ’s often useful use rxode2 syntax, even import NONMEM model using nonmem2rx package. article describes structure classical posologyr models. illustrates define published population models.","code":""},{"path":"https://levenc.github.io/posologyr/articles/classic_posologyr_models.html","id":"structure","dir":"Articles","previous_headings":"","what":"Structure","title":"Classic posologyr models","text":"posologyr model named R list following items: ppk_model rxode2 model implementing structural population model individual model (.e. model inter-individual variability) covariates error_model function residual error model, alternatively named list functions multiple endpoints model vignette(\"multiple_endpoints\") theta named vector population estimates fixed effects parameters (called THETAs, following NONMEM terminology) omega named square variance-covariance matrix population parameters inter-individual variability sigma estimates parameters residual error model pi_matrix Optional. named square variance-covariance matrix population parameters inter-occasion variability covariates character vector covariates model","code":""},{"path":"https://levenc.github.io/posologyr/articles/classic_posologyr_models.html","id":"definition-of-a-prior-model-through-an-example","dir":"Articles","previous_headings":"","what":"Definition of a prior model through an example","title":"Classic posologyr models","text":"model example two-compartment ppk model vancomycin derived retrospective study cohort 1,800 patients (doi:10.1097/FTD.0000000000000490).","code":""},{"path":"https://levenc.github.io/posologyr/articles/classic_posologyr_models.html","id":"ppk_model","dir":"Articles","previous_headings":"Definition of a prior model through an example","what":"ppk_model","title":"Classic posologyr models","text":"model defined rxode2 mini-language. posologyr needs structural model, defined either differential algebraic equations, individual model. Depending model type, naming conventions less strict: Single endpoint model (e.g. pharmacokinetic models) concentration central compartment must named Cc. Multiple endpoints model (eg. PK-PD, parent-metabolite, blood-urine…) names endpoints flexible, must consistent names error models parameters. differential function d/dt(AUC) =  Cc; needed optimization function poso_dose_auc().","code":"ppk_model   = rxode2::rxode({     centr(0) = 0;     TVCl  = THETA_Cl*(CLCREAT/120)^0.8*(0.7^DIAL);     TVVc  = THETA_Vc*(WT/70)          *(0.5^DIAL);     TVVp  = THETA_Vp;     TVQ   = THETA_Q;     Cl    = TVCl*exp(ETA_Cl);     Vc    = TVVc*exp(ETA_Vc);     Vp    = TVVp*exp(ETA_Vp);     Q     = TVQ;     ke    = Cl/Vc;     k12   = Q/Vc;     k21   = Q/Vp;     Cc    = centr/Vc;     d/dt(centr)  = - ke*centr - k12*centr + k21*periph;     d/dt(periph) =            + k12*centr - k21*periph;     d/dt(AUC)    =   Cc;   })"},{"path":"https://levenc.github.io/posologyr/articles/classic_posologyr_models.html","id":"error_model","dir":"Articles","previous_headings":"Definition of a prior model through an example","what":"error_model","title":"Classic posologyr models","text":"function residual error model, taking two arguments: simulated concentrations, vector sigma estimates parameters residual error model. multiple endpoints models, error_model must named list function endpoint vignette(\"multiple_endpoints\"). obtain individual estimations multiple endpoints, consistency naming convention must maintained across following: dataset (using column DVID). residual error models (stored named list). standard deviation residual error models (stored named list, see sigma). many residual error models desired can defined. model defined named list error_models must counterpart named list sigma, names must match defined DVID column dataset.","code":"error_model <- function(f,sigma){           #additive model if sigma[2] == 0   g <- sigma[1]^2 + (sigma[2]^2)*(f^2)      #proportional model if sigma[1] == 0   return(sqrt(g)) } error_model = list(     first_endpoint = function(f,sigma){       g <- sigma[1]^2 + (sigma[2]^2)*(f^2)       return(sqrt(g))     },     second_endpoint = function(f,sigma){       g <- sigma[1]^2 + (sigma[2]^2)*(f^2)       return(sqrt(g))     }   )"},{"path":"https://levenc.github.io/posologyr/articles/classic_posologyr_models.html","id":"theta","dir":"Articles","previous_headings":"Definition of a prior model through an example","what":"theta","title":"Classic posologyr models","text":"estimations parameters fixed effects model (THETA), named vector. names must match names used ppk_model.","code":"theta = c(THETA_Cl=4.5, THETA_Vc=58.4, THETA_Vp=38.4, THETA_Q=6.5)"},{"path":"https://levenc.github.io/posologyr/articles/classic_posologyr_models.html","id":"omega","dir":"Articles","previous_headings":"Definition of a prior model through an example","what":"omega","title":"Classic posologyr models","text":"variance-covariance matrix random effects (ETA) individual model. symmetric matrix. names must match names used ppk_model. easy way define using lotri::lotri(). estimates variances random effects can given different parameterizations depending authors. Standard deviation (SD): square root variance, returned Monolix Coefficient variation (CV): calculated sqrt(exp(SD^2)-1), variance can computed back log((CV^2)+1) Full covariance matrix: easiest reuse, less common literature case vancomycin model, estimates subject variability (BSV) given CV%. must converted variances prior inclusion omega. estimates covariance (-diagonal) sometimes given coefficients correlation ETAs. covariance ETA_a ETA_b can computed following product: standard_deviation(ETA_a) * standard_deviation(ETA_b) * correlation(ETA_a ETA_b). example, covariances equal zero.","code":"omega = lotri::lotri({ETA_Cl + ETA_Vc + ETA_Vp + ETA_Q ~                           c(0.147,                             0    ,  0.510 ,                             0    ,  0     ,   0.282,                             0    ,  0     ,   0    ,    0)})"},{"path":"https://levenc.github.io/posologyr/articles/classic_posologyr_models.html","id":"sigma","dir":"Articles","previous_headings":"Definition of a prior model through an example","what":"sigma","title":"Classic posologyr models","text":"estimates parameters residual error model standard deviation scale, either vector: matrix: named list (multiple endpoints): depending residual error model.","code":"sigma       = c(additive_a = 3.4, proportional_b = 0.227) sigma       = lotri::lotri({prop + add ~ c(0.227,0.0,3.4)}) sigma       = list(     first_endpoint=c(additive_a = 0.144, proportional_b = 0.15),     second_endpoint=c(additive_a = 3.91, proportional_b = 0.0)     )"},{"path":"https://levenc.github.io/posologyr/articles/classic_posologyr_models.html","id":"pi_matrix","dir":"Articles","previous_headings":"Definition of a prior model through an example","what":"pi_matrix","title":"Classic posologyr models","text":"Optional: needed models inter-occasion variability (IOV). variance-covariance matrix random effects (KAPPA) IOV. omega matrix, names must match names used ppk_model. easy way define using lotri::lotri().","code":"pi_matrix = lotri::lotri({KAPPA_Cl + KAPPA_Vc ~       c(0.1934626,         0.00     ,  0.05783106)})"},{"path":"https://levenc.github.io/posologyr/articles/classic_posologyr_models.html","id":"covariates","dir":"Articles","previous_headings":"Definition of a prior model through an example","what":"covariates","title":"Classic posologyr models","text":"names every covariate defined ppk_model, character vector.","code":"covariates  = c(\"CLCREAT\",\"WT\",\"DIAL\")"},{"path":"https://levenc.github.io/posologyr/articles/classic_posologyr_models.html","id":"full-model","dir":"Articles","previous_headings":"Definition of a prior model through an example","what":"Full model","title":"Classic posologyr models","text":"posologyr model list objects. Note: model include inter-occasion variability, pi_matrix omitted.","code":"mod_vancomyin_Goti2018 <- list(   ppk_model   = rxode2::rxode({      centr(0) = 0;     TVCl  = THETA_Cl*(CLCREAT/120)^0.8*(0.7^DIAL);     TVVc  = THETA_Vc*(WT/70)          *(0.5^DIAL);     TVVp  = THETA_Vp;     TVQ   = THETA_Q;     Cl    = TVCl*exp(ETA_Cl);     Vc    = TVVc*exp(ETA_Vc);     Vp    = TVVp*exp(ETA_Vp);     Q     = TVQ;     ke    = Cl/Vc;     k12   = Q/Vc;     k21   = Q/Vp;     Cc    = centr/Vc;     d/dt(centr)  = - ke*centr - k12*centr + k21*periph;     d/dt(periph) =            + k12*centr - k21*periph;     d/dt(AUC)    =   Cc;   }),   error_model = function(f,sigma){     g <- sigma[1] + sigma[2]*f     return(g)   },   theta = c(THETA_Cl=4.5, THETA_Vc=58.4, THETA_Vp=38.4,THETA_Q=6.5),   omega = lotri::lotri({ETA_Cl + ETA_Vc + ETA_Vp + ETA_Q ~                           c(0.147,                             0    ,  0.510 ,                             0    ,  0     ,   0.282,                             0    ,  0     ,   0    ,    0)}),   sigma       = c(additive_a = 3.4, proportional_b = 0.227),   covariates  = c(\"CLCREAT\",\"WT\",\"DIAL\"))"},{"path":"https://levenc.github.io/posologyr/articles/classic_posologyr_models.html","id":"resulting-r-object","dir":"Articles","previous_headings":"Definition of a prior model through an example > Full model","what":"Resulting R object","title":"Classic posologyr models","text":"","code":"mod_vancomyin_Goti2018 #> $ppk_model #> rxode2 NA model named rx_bce7176cfa18100af62e71ae38d3cd48 model (✔ ready).  #> $state: centr, periph, AUC #> $params: THETA_Cl, CLCREAT, DIAL, THETA_Vc, WT, THETA_Vp, THETA_Q, ETA_Cl, ETA_Vc, ETA_Vp #> $lhs: TVCl, TVVc, TVVp, TVQ, Cl, Vc, Vp, Q, ke, k12, k21, Cc #>  #> $error_model #> function(f,sigma){ #>     g <- sigma[1] + sigma[2]*f #>     return(g) #>   } #>  #> $theta #> THETA_Cl THETA_Vc THETA_Vp  THETA_Q  #>      4.5     58.4     38.4      6.5  #>  #> $omega #>        ETA_Cl ETA_Vc ETA_Vp ETA_Q #> ETA_Cl  0.147   0.00  0.000     0 #> ETA_Vc  0.000   0.51  0.000     0 #> ETA_Vp  0.000   0.00  0.282     0 #> ETA_Q   0.000   0.00  0.000     0 #>  #> $sigma #>     additive_a proportional_b  #>          3.400          0.227  #>  #> $covariates #> [1] \"CLCREAT\" \"WT\"      \"DIAL\""},{"path":"https://levenc.github.io/posologyr/articles/multiple_endpoints.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Multiple endpoints","text":"different error model can defined multiple endpoints models (eg. PK-PD, parent-metabolite, blood-urine…). example can seen , utilizing warfarin data model (provided Tomoo Funaki Nick Holford) nlmixr documentation (https://nlmixr2.org/articles/multiple-endpoints.html).","code":""},{"path":"https://levenc.github.io/posologyr/articles/multiple_endpoints.html","id":"warfarin-pkpd-model","dir":"Articles","previous_headings":"Introduction","what":"warfarin PKPD model","title":"Multiple endpoints","text":"","code":"mod_warfarin_nlmixr <- function() {     ini({       #Fixed effects: population estimates        THETA_ktr=0.106       THETA_ka=-0.087       THETA_cl=-2.03       THETA_v=2.07       THETA_emax=3.4       THETA_ec50=0.00724       THETA_kout=-2.9       THETA_e0=4.57              #Random effects: inter-individual variability       ETA_ktr ~ 1.024695       ETA_ka ~ 0.9518403       ETA_cl ~ 0.5300943       ETA_v ~ 0.4785394       ETA_emax ~ 0.7134424       ETA_ec50 ~ 0.7204165       ETA_kout ~ 0.3563706       ETA_e0 ~ 0.2660827              #Unexplained residual variability       cp.sd <- 0.144       cp.prop.sd <- 0.15       pca.sd <- 3.91     })     model({       #Individual model and covariates       ktr <- exp(THETA_ktr + ETA_ktr)       ka <- exp(THETA_ka + ETA_ka)       cl <- exp(THETA_cl + ETA_cl)       v <- exp(THETA_v + ETA_v)       emax = expit(THETA_emax + ETA_emax)       ec50 =  exp(THETA_ec50 + ETA_ec50)       kout = exp(THETA_kout + ETA_kout)       e0 = exp(THETA_e0 + ETA_e0)              #Structural model defined using ordinary differential equations (ODE)       DCP = center/v       PD=1-emax*DCP/(ec50+DCP)              effect(0) = e0       kin = e0*kout              d/dt(depot) = -ktr * depot       d/dt(gut) =  ktr * depot -ka * gut       d/dt(center) =  ka * gut - cl / v * center       d/dt(effect) = kin*PD -kout*effect              cp = center / v       pca = effect              #Model for unexplained residual variability       cp ~ add(cp.sd) + prop(cp.prop.sd)       pca ~ add(pca.sd)     })   }"},{"path":"https://levenc.github.io/posologyr/articles/multiple_endpoints.html","id":"data-first-subject-from-the-warfarin-dataset","dir":"Articles","previous_headings":"Introduction","what":"data: first subject from the warfarin dataset","title":"Multiple endpoints","text":"posologyr can compute EBE combined PKPD model poso_estim_map() observation/time curves endpoints can also plotted","code":"warf_01 <- data.frame(ID=1,                       TIME=c(0.0,1.0,3.0,6.0,24.0,24.0,36.0,36.0,48.0,48.0,72.0,72.0,144.0),                       DV=c(0.0,1.9,6.6,10.8,5.6,44.0,4.0,27.0,2.7,28.0,0.8,31.0,71.0),                       DVID=c(\"cp\",\"cp\",\"cp\",\"cp\",\"cp\",\"pca\",\"cp\",\"pca\",\"cp\",\"pca\",\"cp\",\"pca\",\"pca\"),                       EVID=c(1,0,0,0,0,0,0,0,0,0,0,0,0),                       AMT=c(100,0,0,0,0,0,0,0,0,0,0,0,0)) warf_01 #>    ID TIME   DV DVID EVID AMT #> 1   1    0  0.0   cp    1 100 #> 2   1    1  1.9   cp    0   0 #> 3   1    3  6.6   cp    0   0 #> 4   1    6 10.8   cp    0   0 #> 5   1   24  5.6   cp    0   0 #> 6   1   24 44.0  pca    0   0 #> 7   1   36  4.0   cp    0   0 #> 8   1   36 27.0  pca    0   0 #> 9   1   48  2.7   cp    0   0 #> 10  1   48 28.0  pca    0   0 #> 11  1   72  0.8   cp    0   0 #> 12  1   72 31.0  pca    0   0 #> 13  1  144 71.0  pca    0   0 map_warf_01  <- poso_estim_map(warf_01,mod_warfarin_nlmixr) map_warf_01 #> $eta #>     ETA_ktr      ETA_ka      ETA_cl       ETA_v    ETA_emax    ETA_ec50  #> -0.32052193 -0.54227235  0.79432182 -0.02986447  0.02383273 -0.28133882  #>    ETA_kout      ETA_e0  #> -0.30872226 -0.08386652  #>  #> $model #> ── Solved rxode2 object ── #> ── Parameters ($params): ── #>   THETA_ktr    THETA_ka    THETA_cl     THETA_v  THETA_emax  THETA_ec50  #>  0.10600000 -0.08700000 -2.03000000  2.07000000  3.40000000  0.00724000  #>  THETA_kout    THETA_e0       cp.sd  cp.prop.sd      pca.sd     ETA_ktr  #> -2.90000000  4.57000000  0.14400000  0.15000000  3.91000000 -0.32052193  #>      ETA_ka      ETA_cl       ETA_v    ETA_emax    ETA_ec50    ETA_kout  #> -0.54227235  0.79432182 -0.02986447  0.02383273 -0.28133882 -0.30872226  #>      ETA_e0  #> -0.08386652  #> ── Initial Conditions ($inits): ── #>  depot    gut center effect  #>      0      0      0      0  #> ── First part of data (object): ── #> # A tibble: 1,451 × 18 #>    time   ktr    ka    cl     v  emax  ec50   kout    e0    DCP    PD   kin #>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl> <dbl>  <dbl> <dbl> <dbl> #> 1   0   0.807 0.533 0.291  7.69 0.968 0.760 0.0404  88.8 0      1      3.59 #> 2   0.1 0.807 0.533 0.291  7.69 0.968 0.760 0.0404  88.8 0.0267 0.967  3.59 #> 3   0.2 0.807 0.533 0.291  7.69 0.968 0.760 0.0404  88.8 0.102  0.885  3.59 #> 4   0.3 0.807 0.533 0.291  7.69 0.968 0.760 0.0404  88.8 0.219  0.783  3.59 #> 5   0.4 0.807 0.533 0.291  7.69 0.968 0.760 0.0404  88.8 0.373  0.681  3.59 #> 6   0.5 0.807 0.533 0.291  7.69 0.968 0.760 0.0404  88.8 0.557  0.590  3.59 #> # ℹ 1,445 more rows #> # ℹ 6 more variables: cp <dbl>, pca <dbl>, depot <dbl>, gut <dbl>, #> #   center <dbl>, effect <dbl> #>  #> $event #>          id  time   amt  evid #>       <int> <num> <num> <int> #>    1:     1   0.0    NA     0 #>    2:     1   0.0   100     1 #>    3:     1   0.1    NA     0 #>    4:     1   0.2    NA     0 #>    5:     1   0.3    NA     0 #>   ---                         #> 1448:     1 144.6    NA     0 #> 1449:     1 144.7    NA     0 #> 1450:     1 144.8    NA     0 #> 1451:     1 144.9    NA     0 #> 1452:     1 145.0    NA     0 plot(map_warf_01$model,\"cp\") plot(map_warf_01$model,\"pca\")"},{"path":"https://levenc.github.io/posologyr/articles/patient_data.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Patient data","text":"describes structure patient records compatible posologyr.","code":""},{"path":"https://levenc.github.io/posologyr/articles/patient_data.html","id":"structure","dir":"Articles","previous_headings":"","what":"Structure","title":"Patient data","text":"Data input posologyr type data input rxode2. stated rxode2 documentation (rxode2 datasets), also similar data NONMEM. patient dataset table sequential event records, line event. description different event types available rxode2 documentation (rxode2 Event Types). minimal working example:","code":"data.frame(ID=1,            TIME=c(0.0,3),            DV=c(NA,60.0),            AMT=c(1000,0),            EVID=c(101,0)) #>   ID TIME DV  AMT EVID #> 1  1    0 NA 1000  101 #> 2  1    3 60    0    0"},{"path":"https://levenc.github.io/posologyr/articles/patient_data.html","id":"required-fields-and-data","dir":"Articles","previous_headings":"Structure","what":"Required fields and data","title":"Patient data","text":"TIME Dosing times, sampling times therapeutic drug monitoring (TDM). units depend specification population pharmacokinetics (ppk) model. AMT Amount drug administered. units depend specification ppk model. EVID Event type. Must 0 observations (concentrations TDM). DV concentrations. Must NA EVID 0. Covariates Every covariate defined prior posologyr model. column names must match covariate vector ppk model. OCC Occasions. Required models inter-occasion variability. DVID Observation type. Required models multiple endpoints. CMT Name (number) compartment dose administered, see vignette(\"route_of_administration\").","code":""},{"path":[]},{"path":"https://levenc.github.io/posologyr/articles/patient_data.html","id":"a-priori-dose-adjustment","dir":"Articles","previous_headings":"Common use cases","what":"A priori dose adjustment","title":"Patient data","text":"dosing: simplest patient record single line dataframe, individual patient covariates, columns (TIME, DV, AMT, EVID) set zero.","code":"data.frame(ID=1,            TIME=0,            DV=0,            AMT=0,            EVID=0,            COVAR1=c(\"X\"),            COVAR2=c(\"Y\")) #>   ID TIME DV AMT EVID COVAR1 COVAR2 #> 1  1    0  0   0    0      X      Y"},{"path":"https://levenc.github.io/posologyr/articles/patient_data.html","id":"oral-administration-or-iv-bolus","dir":"Articles","previous_headings":"Common use cases","what":"Oral administration or IV bolus","title":"Patient data","text":"EVID = 1 instantaneous administration first compartment defined ppk model: either central compartment IV bolus, depot compartment oral administration.","code":"data.frame(ID=1,            TIME=c(0.0,3),            DV=c(NA,60.0),            AMT=c(1000,0),            EVID=c(1,0),            COVAR1=c(\"X\"),            COVAR2=c(\"Y\")) #>   ID TIME DV  AMT EVID COVAR1 COVAR2 #> 1  1    0 NA 1000    1      X      Y #> 2  1    3 60    0    0      X      Y"},{"path":"https://levenc.github.io/posologyr/articles/patient_data.html","id":"intermittent-infusion","dir":"Articles","previous_headings":"Common use cases","what":"Intermittent infusion","title":"Patient data","text":"EVID = 1 bolus infusion, administered first compartment defined ppk model. DUR defines duration. AMT amount administered duration DUR.","code":"data.frame(ID=1,            TIME=c(0.0,1.0,14.0),            DV=c(NA,25.0,5.5),            AMT=c(1000,0,0),            DUR=c(0.5,NA,NA),            EVID=c(1,0,0),            COVAR1=c(\"X\"),            COVAR2=c(\"Y\")) #>   ID TIME   DV  AMT DUR EVID COVAR1 COVAR2 #> 1  1    0   NA 1000 0.5    1      X      Y #> 2  1    1 25.0    0  NA    0      X      Y #> 3  1   14  5.5    0  NA    0      X      Y"},{"path":"https://levenc.github.io/posologyr/articles/patient_data.html","id":"inter-occasion-variability","dir":"Articles","previous_headings":"Common use cases","what":"Inter-occasion variability","title":"Patient data","text":"given occasion, value OCC must repeated row. OCC must specified rows table.","code":"data.frame(ID=1,            TIME=c(0.0,1.0,14.0,24.0,25.0,36.0),            DV=c(NA,25.0,5.5,NA,30.0,6.0),            AMT=c(1000,0,0,1000,0,0),            DUR=c(0.5,NA,NA,0.5,NA,NA),            EVID=c(1,0,0,1,0,0),            OCC=c(1,1,1,2,2,2),            COVAR1=c(\"X\"),            COVAR2=c(\"Y\")) #>   ID TIME   DV  AMT DUR EVID OCC COVAR1 COVAR2 #> 1  1    0   NA 1000 0.5    1   1      X      Y #> 2  1    1 25.0    0  NA    0   1      X      Y #> 3  1   14  5.5    0  NA    0   1      X      Y #> 4  1   24   NA 1000 0.5    1   2      X      Y #> 5  1   25 30.0    0  NA    0   2      X      Y #> 6  1   36  6.0    0  NA    0   2      X      Y"},{"path":"https://levenc.github.io/posologyr/articles/patient_data.html","id":"multiple-endpoints","dir":"Articles","previous_headings":"Common use cases","what":"Multiple endpoints","title":"Patient data","text":"DVID used specify type observation. values DVID dataset must match names residual error models, see vignette(\"multiple_endpoints\").","code":"data.frame(ID=1,            TIME=c(0.0,1.0,14.0,24.0,25.0,36.0),            DV=c(NA,20.0,80,35.5,60.0,40.0),            AMT=c(1000,0,0,0,0,0),            EVID=c(1,0,0,0,0,0),            DVID=c(\"parent\",\"parent\",\"metabolite\",\"parent\",\"metabolite\",\"metabolite\"),            COVAR1=c(\"X\"),            COVAR2=c(\"Y\")) #>   ID TIME   DV  AMT EVID       DVID COVAR1 COVAR2 #> 1  1    0   NA 1000    1     parent      X      Y #> 2  1    1 20.0    0    0     parent      X      Y #> 3  1   14 80.0    0    0 metabolite      X      Y #> 4  1   24 35.5    0    0     parent      X      Y #> 5  1   25 60.0    0    0 metabolite      X      Y #> 6  1   36 40.0    0    0 metabolite      X      Y"},{"path":"https://levenc.github.io/posologyr/articles/patient_data_input.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Patient records","text":"describes structure patient records usable posologyr.","code":""},{"path":"https://levenc.github.io/posologyr/articles/patient_data_input.html","id":"structure","dir":"Articles","previous_headings":"","what":"Structure","title":"Patient records","text":"Data input posologyr type data input rxode2. stated rxode2 documentation (rxode2 datasets), also similar data NONMEM. patient dataset table sequential event records, line event. description different event types available rxode2 documentation (rxode2 Event Types). minimal working example:","code":"data.frame(ID=1,            TIME=c(0.0,3),            DV=c(NA,60.0),            AMT=c(1000,0),            EVID=c(101,0)) #>   ID TIME DV  AMT EVID #> 1  1    0 NA 1000  101 #> 2  1    3 60    0    0"},{"path":"https://levenc.github.io/posologyr/articles/patient_data_input.html","id":"required-fields-and-data","dir":"Articles","previous_headings":"Structure","what":"Required fields and data","title":"Patient records","text":"TIME Dosing times, sampling times therapeutic drug monitoring (TDM). units depend specification population pharmacokinetics (ppk) model. AMT Amount drug administered. units depend specification ppk model. EVID Event type. Must 0 observations (concentrations TDM). DV concentrations. Must NA EVID 0. Covariates Every covariate defined prior posologyr model. column names must match covariate vector ppk model. OCC Occasions. Required models inter-occasion variability. DVID Observation type. Required models multiple endpoints. CMT Name (number) compartment dose administered, see vignette(\"route_of_administration\").","code":""},{"path":[]},{"path":"https://levenc.github.io/posologyr/articles/patient_data_input.html","id":"a-priori-dose-adjustment","dir":"Articles","previous_headings":"Common use cases","what":"A priori dose adjustment","title":"Patient records","text":"dosing: simplest patient record single line dataframe, individual patient covariates, columns (TIME, DV, AMT, EVID) set zero.","code":"data.frame(ID=1,            TIME=0,            DV=0,            AMT=0,            EVID=0,            COVAR1=c(\"X\"),            COVAR2=c(\"Y\")) #>   ID TIME DV AMT EVID COVAR1 COVAR2 #> 1  1    0  0   0    0      X      Y"},{"path":"https://levenc.github.io/posologyr/articles/patient_data_input.html","id":"oral-administration-or-iv-bolus","dir":"Articles","previous_headings":"Common use cases","what":"Oral administration or IV bolus","title":"Patient records","text":"EVID = 1 instantaneous administration first compartment defined ppk model: either central compartment IV bolus, depot compartment oral administration.","code":"data.frame(ID=1,            TIME=c(0.0,3),            DV=c(NA,60.0),            AMT=c(1000,0),            EVID=c(1,0),            COVAR1=c(\"X\"),            COVAR2=c(\"Y\")) #>   ID TIME DV  AMT EVID COVAR1 COVAR2 #> 1  1    0 NA 1000    1      X      Y #> 2  1    3 60    0    0      X      Y"},{"path":"https://levenc.github.io/posologyr/articles/patient_data_input.html","id":"intermittent-infusion","dir":"Articles","previous_headings":"Common use cases","what":"Intermittent infusion","title":"Patient records","text":"EVID = 1 bolus infusion, administered first compartment defined ppk model. DUR defines duration. AMT amount administered duration DUR.","code":"data.frame(ID=1,            TIME=c(0.0,1.0,14.0),            DV=c(NA,25.0,5.5),            AMT=c(1000,0,0),            DUR=c(0.5,NA,NA),            EVID=c(1,0,0),            COVAR1=c(\"X\"),            COVAR2=c(\"Y\")) #>   ID TIME   DV  AMT DUR EVID COVAR1 COVAR2 #> 1  1    0   NA 1000 0.5    1      X      Y #> 2  1    1 25.0    0  NA    0      X      Y #> 3  1   14  5.5    0  NA    0      X      Y"},{"path":"https://levenc.github.io/posologyr/articles/patient_data_input.html","id":"inter-occasion-variability","dir":"Articles","previous_headings":"Common use cases","what":"Inter-occasion variability","title":"Patient records","text":"given occasion, value OCC must repeated row. OCC must specified rows table.","code":"data.frame(ID=1,            TIME=c(0.0,1.0,14.0,24.0,25.0,36.0),            DV=c(NA,25.0,5.5,NA,30.0,6.0),            AMT=c(1000,0,0,1000,0,0),            DUR=c(0.5,NA,NA,0.5,NA,NA),            EVID=c(1,0,0,1,0,0),            OCC=c(1,1,1,2,2,2),            COVAR1=c(\"X\"),            COVAR2=c(\"Y\")) #>   ID TIME   DV  AMT DUR EVID OCC COVAR1 COVAR2 #> 1  1    0   NA 1000 0.5    1   1      X      Y #> 2  1    1 25.0    0  NA    0   1      X      Y #> 3  1   14  5.5    0  NA    0   1      X      Y #> 4  1   24   NA 1000 0.5    1   2      X      Y #> 5  1   25 30.0    0  NA    0   2      X      Y #> 6  1   36  6.0    0  NA    0   2      X      Y"},{"path":"https://levenc.github.io/posologyr/articles/patient_data_input.html","id":"multiple-endpoints","dir":"Articles","previous_headings":"Common use cases","what":"Multiple endpoints","title":"Patient records","text":"DVID used specify type observation. values DVID dataset must match names residual error models, see vignette(\"multiple_endpoints\").","code":"data.frame(ID=1,            TIME=c(0.0,1.0,14.0,24.0,25.0,36.0),            DV=c(NA,20.0,80,35.5,60.0,40.0),            AMT=c(1000,0,0,0,0,0),            EVID=c(1,0,0,0,0,0),            DVID=c(\"parent\",\"parent\",\"metabolite\",\"parent\",\"metabolite\",\"metabolite\"),            COVAR1=c(\"X\"),            COVAR2=c(\"Y\")) #>   ID TIME   DV  AMT EVID       DVID COVAR1 COVAR2 #> 1  1    0   NA 1000    1     parent      X      Y #> 2  1    1 20.0    0    0     parent      X      Y #> 3  1   14 80.0    0    0 metabolite      X      Y #> 4  1   24 35.5    0    0     parent      X      Y #> 5  1   25 60.0    0    0 metabolite      X      Y #> 6  1   36 40.0    0    0 metabolite      X      Y"},{"path":"https://levenc.github.io/posologyr/articles/population_models.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Population models","text":"describes structure prior population models compatible posologyr illustrates define new models published population models.","code":""},{"path":"https://levenc.github.io/posologyr/articles/population_models.html","id":"general-structure","dir":"Articles","previous_headings":"","what":"General structure","title":"Population models","text":"models can written model function can parsed rxode2. models written two block statements: ini({}) defining parameter values model({}) ODE-based model specification. example, gentamicin model Xuan et al. 2003 (doi:10.1016/j.ijantimicag.2003.07.010) can written : rxode2 mini-language syntax detailed rxode2 documentation.","code":"mod_gentamicin_Xuan2003 <- function() {   ini({     #Fixed effects: population estimates      THETA_Cl  = 0.047     THETA_V   = 0.28     THETA_k12 = 0.092     THETA_k21 = 0.071          #Random effects: inter-individual variability     ETA_Cl  ~ 0.084     ETA_V   ~ 0.003     ETA_k12 ~ 0.398     ETA_k21 ~ 0.342          #Unexplained residual variability     add_sd  <- 0.230     prop_sd <- 0.237   })   model({     #Individual model and covariates     TVl   = THETA_Cl*ClCr     TVV   = THETA_V*WT     TVk12 = THETA_k12     TVk21 = THETA_k21     Cl    = TVl*exp(ETA_Cl)     V     = TVV*exp(ETA_V)     k12   = TVk12*exp(ETA_k12)     k21   = TVk21*exp(ETA_k21)          #Structural model defined using ordinary differential equations (ODE)     ke    = Cl/V     Cp    = centr/V      d/dt(centr)  = - ke*centr - k12*centr + k21*periph     d/dt(periph) =            + k12*centr - k21*periph      #Model for unexplained residual variability     Cp ~ add(add_sd) + prop(prop_sd) + combined1()   }) }"},{"path":"https://levenc.github.io/posologyr/articles/population_models.html","id":"individual-model-random-effects","dir":"Articles","previous_headings":"","what":"Individual model, random effects","title":"Population models","text":"Inter-individual variability can also defined symmetric matrix integrate covariance random effects. example, amikacin model Burdet et al. 2015 (doi:10.1007/s00228-014-1766-y) can written : estimates variances random effects can given different parameterizations depending authors. Standard deviation (SD): square root variance, returned Monolix Coefficient variation (CV): calculated sqrt(exp(SD^2)-1), variance can computed back log((CV^2)+1) Full covariance matrix: easiest reuse, less common literature estimates covariance (diagonal) sometimes given coefficients correlation ETAs. covariance ETA_a ETA_b can computed following product: standard_deviation(ETA_a) * standard_deviation(ETA_b) * correlation(ETA_a ETA_b).","code":"mod_amikacin_Burdet2015 <- function() {     ini({       #Fixed effects: population estimates        THETA_Cl=4.3       THETA_Vc=15.9       THETA_Vp=21.4       THETA_Q=12.1              #Random effects: inter-individual variability       ETA_Cl + ETA_Vc + ETA_Vp + ETA_Q ~         c(0.1,           0.01     ,    0.05 ,           0.01     ,    0.02 ,   0.2  ,          -0.06     ,    0.004,   0.003,    0.08)                  #Unexplained residual variability       add_sd <- 0.2       prop_sd <- 0.1     })     model({       #Individual model and covariates       TVCl  = THETA_Cl*(CLCREAT4H/82)^0.7       TVVc  = THETA_Vc*(TBW/78)^0.9*(PoverF/169)^0.4       TVVp  = THETA_Vp       TVQ   = THETA_Q       Cl    = TVCl*exp(ETA_Cl)       Vc    = TVVc*exp(ETA_Vc)       Vp    = TVVp*exp(ETA_Vp)       Q     = TVQ *exp(ETA_Q)              #Structural model defined using ordinary differential equations (ODE)       ke    = Cl/Vc       k12   = Q/Vc       k21   = Q/Vp       Cp    = centr/Vc                    d/dt(centr)  = - ke*centr - k12*centr + k21*periph       d/dt(periph) =            + k12*centr - k21*periph        #Model for unexplained residual variability       Cp ~ add(add_sd) + prop(prop_sd) + combined1()     })   }"},{"path":"https://levenc.github.io/posologyr/articles/population_models.html","id":"bioavailability-lag-time","dir":"Articles","previous_headings":"","what":"Bioavailability, lag-time","title":"Population models","text":"Special model event changes including bioavailability (f(depot)=1), lag time (alag(depot)=0) can used model({}) block. example, ganciclovir model Caldès et al. 2009 (doi:10.1128/aac.00085-09) can written :","code":"mod_ganciclovir_Caldes2009 <- function() {   ini({     #Fixed effects: population estimates      THETA_cl  <- 7.49     THETA_v1  <- 31.90     THETA_cld <- 10.20     THETA_v2  <- 32.0     THETA_ka  <- 0.895     THETA_baf <- 0.825            #Random effects: inter-individual variability     ETA_cl ~ 0.107     ETA_v1 ~ 0.227     ETA_ka ~ 0.464     ETA_baf ~ 0.049          #Unexplained residual variability     add.sd <- 0.465     prop.sd <- 0.143   })   model({     #Individual model and covariates     TVcl  = THETA_cl*(ClCr/57);     TVv1  = THETA_v1;     TVcld = THETA_cld;     TVv2  = THETA_v2;     TVka  = THETA_ka;     TVbaf = THETA_baf;     cl  = TVcl*exp(ETA_cl);     v1  = TVv1*exp(ETA_v1);     cld = TVcld;     v2  = TVv2;     ka  = TVka*exp(ETA_ka);     baf = TVbaf*exp(ETA_baf);      #Structural model defined using ordinary differential equations (ODE)     k10 = cl/v1;     k12 = cld / v1;     k21 = cld / v2;     Cc = centr/v1;      d/dt(depot)  = -ka*depot     d/dt(centr)  =  ka*depot - k10*centr - k12*centr + k21*periph;     d/dt(periph) =                         k12*centr - k21*periph;     d/dt(AUC)    = Cc;      #Special model event changes     f(depot)=baf;     alag(depot)=0.382;          #Model for unexplained residual variability     Cc ~ add(add.sd) + prop(prop.sd) + combined1()   }) }"},{"path":"https://levenc.github.io/posologyr/articles/population_models.html","id":"classic-posologyr-models","dir":"Articles","previous_headings":"","what":"Classic posologyr models","title":"Population models","text":"models described rxode2 model function alone. possible define classic posologyr model, see vignette (\"classic_posologyr_models\"). Models falling category : models inter-occasion variability (IOV), sometimes called intra-individual variability models unexplained residual error model additive, proportional, combined models","code":""},{"path":"https://levenc.github.io/posologyr/articles/posologyr_user_defined_models.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"User defined models","text":"describes structure prior models usable posologyr illustrates define new models published population pharmacokinetic (ppk) models.","code":""},{"path":"https://levenc.github.io/posologyr/articles/posologyr_user_defined_models.html","id":"structure","dir":"Articles","previous_headings":"","what":"Structure","title":"User defined models","text":"posologyr prior ppk model named R list: ppk_model rxode2 model implementing structural population pharmacokinetics model individual model (.e. model inter-individual variability) covariates error_model function residual error model, alternatively named list functions multiple endpoints model vignette(\"multiple_endpoints\") theta named vector population estimates fixed effects parameters (called THETAs, following NONMEM terminology) omega named square variance-covariance matrix population parameters inter-individual variability sigma estimates parameters residual error model pi_matrix Optional. named square variance-covariance matrix population parameters inter-occasion variability covariates character vector covariates model","code":""},{"path":"https://levenc.github.io/posologyr/articles/posologyr_user_defined_models.html","id":"definition-of-a-prior-model-through-an-example","dir":"Articles","previous_headings":"","what":"Definition of a prior model through an example","title":"User defined models","text":"model implement two-compartment ppk model vancomycin derived retrospective study cohort 1,800 patients (doi:10.1097/FTD.0000000000000490).","code":""},{"path":"https://levenc.github.io/posologyr/articles/posologyr_user_defined_models.html","id":"ppk_model","dir":"Articles","previous_headings":"Definition of a prior model through an example","what":"ppk_model","title":"User defined models","text":"model defined rxode2::rxode() mini-language. posologyr needs structural model, defined either differential algebraic equations, individual model. concentration central compartment must named Cc. differential function d/dt(AUC) =  Cc; needed optimisation function poso_dose_auc().","code":"ppk_model   = rxode2::rxode({     centr(0) = 0;     TVCl  = THETA_Cl*(CLCREAT/120)^0.8*(0.7^DIAL);     TVVc  = THETA_Vc*(WT/70)          *(0.5^DIAL);     TVVp  = THETA_Vp;     TVQ   = THETA_Q;     Cl    = TVCl*exp(ETA_Cl);     Vc    = TVVc*exp(ETA_Vc);     Vp    = TVVp*exp(ETA_Vp);     Q     = TVQ;     ke    = Cl/Vc;     k12   = Q/Vc;     k21   = Q/Vp;     Cc    = centr/Vc;     d/dt(centr)  = - ke*centr - k12*centr + k21*periph;     d/dt(periph) =            + k12*centr - k21*periph;     d/dt(AUC)    =   Cc;   })"},{"path":"https://levenc.github.io/posologyr/articles/posologyr_user_defined_models.html","id":"error_model","dir":"Articles","previous_headings":"Definition of a prior model through an example","what":"error_model","title":"User defined models","text":"function residual error model, taking two arguments: simulated concentrations, vector sigma estimates parameters residual error model. Alternatively, function can take simulated concentrations, matrix sigma estimates parameters residual error model, following example: multiple endpoint models, error_model must named list function endpoint vignette(\"multiple_endpoints\").","code":"error_model <- function(f,sigma){     #additive model if sigma[2] == 0   g <- sigma[1] + sigma[2]*f          #proportional model if sigma[1] == 0   return(g) } error_model <- function(f,sigma){   dv <- cbind(f,1)   g  <- diag(dv%*%sigma%*%t(dv))     #sigma is the square matrix of the residual   return(sqrt(g))                    #errors }"},{"path":"https://levenc.github.io/posologyr/articles/posologyr_user_defined_models.html","id":"theta","dir":"Articles","previous_headings":"Definition of a prior model through an example","what":"theta","title":"User defined models","text":"estimations parameters fixed effects model (THETA), named vector. names must match names used ppk_model.","code":"theta = c(THETA_Cl=4.5, THETA_Vc=58.4, THETA_Vp=38.4, THETA_Q=6.5)"},{"path":"https://levenc.github.io/posologyr/articles/posologyr_user_defined_models.html","id":"omega","dir":"Articles","previous_headings":"Definition of a prior model through an example","what":"omega","title":"User defined models","text":"variance-covariance matrix random effects (ETA) individual model. symmetric matrix. names must match names used ppk_model. easy way define using lotri::lotri(). estimates variances random effects can given different parameterizations depending authors. Standard deviation (SD): square root variance, returned Monolix Coefficient variation (CV): calculated sqrt(exp(SD^2)-1), standard deviation can computed back sqrt(log((CV^2)+1)) Full covariance matrix: easiest reuse, rarely seen articles case vancomycin model, estimates subject variability (BSV) given CV%. must converted variances prior inclusion omega. estimates covariance (diagonal) sometimes given coefficients correlation ETAs. covariance ETA_a ETA_b can computed following product: standard_deviation(ETA_a) * standard_deviation(ETA_b) * correlation(ETA_a ETA_b). example, covariances equal zero.","code":"omega = lotri::lotri({ETA_Cl + ETA_Vc + ETA_Vp + ETA_Q ~                           c(0.147,                             0    ,  0.510 ,                             0    ,  0     ,   0.282,                             0    ,  0     ,   0    ,    0)})"},{"path":"https://levenc.github.io/posologyr/articles/posologyr_user_defined_models.html","id":"sigma","dir":"Articles","previous_headings":"Definition of a prior model through an example","what":"sigma","title":"User defined models","text":"estimates parameters residual error model, either vector: matrix: named list, see vignette(\"multiple_endpoints\"): depending residual error model.","code":"sigma       = c(additive_a = 3.4, proportional_b = 0.227) sigma       = lotri::lotri({prop + add ~ c(0.227,0.0,3.4)}) sigma       = list(     cp=c(additive_a = 0.144, proportional_b = 0.15),     pca=c(additive_a = 3.91, proportional_b = 0.0)     )"},{"path":"https://levenc.github.io/posologyr/articles/posologyr_user_defined_models.html","id":"pi_matrix","dir":"Articles","previous_headings":"Definition of a prior model through an example","what":"pi_matrix","title":"User defined models","text":"Optional: needed models inter-occasion variability (IOV). variance-covariance matrix random effects (KAPPA) IOV. omega matrix, names must match names used ppk_model. easy way define using lotri::lotri().","code":"pi_matrix = lotri::lotri({KAPPA_Cl + KAPPA_Vc ~       c(0.1934626,         0.00     ,  0.05783106)})"},{"path":"https://levenc.github.io/posologyr/articles/posologyr_user_defined_models.html","id":"covariates","dir":"Articles","previous_headings":"Definition of a prior model through an example","what":"covariates","title":"User defined models","text":"names every covariate defined ppk_model, character vector.","code":"covariates  = c(\"CLCREAT\",\"WT\",\"DIAL\")"},{"path":"https://levenc.github.io/posologyr/articles/posologyr_user_defined_models.html","id":"full-model","dir":"Articles","previous_headings":"Definition of a prior model through an example","what":"Full model","title":"User defined models","text":"posologyr model list objects. Note: model include inter-occasion variability, pi_matrix omitted.","code":"mod_vancomyin_Goti2018 <- list(   ppk_model   = rxode2::rxode({      centr(0) = 0;     TVCl  = THETA_Cl*(CLCREAT/120)^0.8*(0.7^DIAL);     TVVc  = THETA_Vc*(WT/70)          *(0.5^DIAL);     TVVp  = THETA_Vp;     TVQ   = THETA_Q;     Cl    = TVCl*exp(ETA_Cl);     Vc    = TVVc*exp(ETA_Vc);     Vp    = TVVp*exp(ETA_Vp);     Q     = TVQ;     ke    = Cl/Vc;     k12   = Q/Vc;     k21   = Q/Vp;     Cc    = centr/Vc;     d/dt(centr)  = - ke*centr - k12*centr + k21*periph;     d/dt(periph) =            + k12*centr - k21*periph;     d/dt(AUC)    =   Cc;   }),   error_model = function(f,sigma){     g <- sigma[1] + sigma[2]*f     return(g)   },   theta = c(THETA_Cl=4.5, THETA_Vc=58.4, THETA_Vp=38.4,THETA_Q=6.5),   omega = lotri::lotri({ETA_Cl + ETA_Vc + ETA_Vp + ETA_Q ~                           c(0.147,                             0    ,  0.510 ,                             0    ,  0     ,   0.282,                             0    ,  0     ,   0    ,    0)}),   sigma       = c(additive_a = 3.4, proportional_b = 0.227),   covariates  = c(\"CLCREAT\",\"WT\",\"DIAL\"))"},{"path":"https://levenc.github.io/posologyr/articles/posologyr_user_defined_models.html","id":"resulting-r-object","dir":"Articles","previous_headings":"Definition of a prior model through an example > Full model","what":"Resulting R object","title":"User defined models","text":"","code":"mod_vancomyin_Goti2018 #> $ppk_model #> rxode2 NA model named rx_bce7176cfa18100af62e71ae38d3cd48 model (✔ ready).  #> $state: centr, periph, AUC #> $params: THETA_Cl, CLCREAT, DIAL, THETA_Vc, WT, THETA_Vp, THETA_Q, ETA_Cl, ETA_Vc, ETA_Vp #> $lhs: TVCl, TVVc, TVVp, TVQ, Cl, Vc, Vp, Q, ke, k12, k21, Cc #>  #> $error_model #> function(f,sigma){ #>     g <- sigma[1] + sigma[2]*f #>     return(g) #>   } #>  #> $theta #> THETA_Cl THETA_Vc THETA_Vp  THETA_Q  #>      4.5     58.4     38.4      6.5  #>  #> $omega #>        ETA_Cl ETA_Vc ETA_Vp ETA_Q #> ETA_Cl  0.147   0.00  0.000     0 #> ETA_Vc  0.000   0.51  0.000     0 #> ETA_Vp  0.000   0.00  0.282     0 #> ETA_Q   0.000   0.00  0.000     0 #>  #> $sigma #>     additive_a proportional_b  #>          3.400          0.227  #>  #> $covariates #> [1] \"CLCREAT\" \"WT\"      \"DIAL\""},{"path":"https://levenc.github.io/posologyr/articles/route_of_administration.html","id":"introduction","dir":"Articles","previous_headings":"","what":"Introduction","title":"Route of administration","text":"Caldès 2009 ganciclovir model (https://doi.org/10.1128/aac.00085-09) capable describing pharmacokinetics either injectable ganciclovir oral valganciclovir.","code":"mod_ganciclovir_Caldes_2009 <- function() {   ini({     THETA_cl  <- 7.49     THETA_v1  <- 31.90     THETA_cld <- 10.20     THETA_v2  <- 32.0     THETA_ka  <- 0.895     THETA_baf <- 0.825     ETA_cl ~ 0.107     ETA_v1 ~ 0.227     ETA_ka ~ 0.464     ETA_baf ~ 0.049     add.sd <- 0.465     prop.sd <- 0.143   })   model({     TVcl  = THETA_cl*(ClCr/57);     TVv1  = THETA_v1;     TVcld = THETA_cld;     TVv2  = THETA_v2;     TVka  = THETA_ka;     TVbaf = THETA_baf;      cl  = TVcl*exp(ETA_cl);     v1  = TVv1*exp(ETA_v1);     cld = TVcld;     v2  = TVv2;     ka  = TVka*exp(ETA_ka);     baf = TVbaf*exp(ETA_baf);      k10 = cl/v1;     k12 = cld / v1;     k21 = cld / v2;     Cc = centr/v1;      d/dt(depot)  = -ka*depot     d/dt(centr)  =  ka*depot - k10*centr - k12*centr + k21*periph;     d/dt(periph) =                         k12*centr - k21*periph;     d/dt(AUC)    = Cc;      f(depot)=baf;     alag(depot)=0.382;      Cc ~ add(add.sd) + prop(prop.sd) + combined1()   }) }"},{"path":[]},{"path":"https://levenc.github.io/posologyr/articles/route_of_administration.html","id":"patient-record-with-tdm-data","dir":"Articles","previous_headings":"Intravenous ganciclovir","what":"Patient record with TDM data","title":"Route of administration","text":"describe intravenous administration, CMT column added TDM data table indicate administrations directly central compartment. Note: compute AUC last dose time last dose + 24 hours, dummy dose 0 mg added time last observation interest (.e. H144).","code":"patient <- data.frame(ID=1,TIME=c(0,121,122,126,144),                       DV=c(NA,10.8,5.8,3.3,NA),                       ADDL=c(5,0,0,0,0),                       II=c(24,0,0,0,0),                       EVID=c(1,0,0,0,1),                       CMT=c(\"centr\",NA,NA,NA,\"centr\"),                       AMT=c(250,0,0,0,0),                       DUR=c(0.5,NA,NA,NA,NA),                       ClCr=25) patient #>   ID TIME   DV ADDL II EVID   CMT AMT DUR ClCr #> 1  1    0   NA    5 24    1 centr 250 0.5   25 #> 2  1  121 10.8    0  0    0  <NA>   0  NA   25 #> 3  1  122  5.8    0  0    0  <NA>   0  NA   25 #> 4  1  126  3.3    0  0    0  <NA>   0  NA   25 #> 5  1  144   NA    0  0    1 centr   0  NA   25"},{"path":"https://levenc.github.io/posologyr/articles/route_of_administration.html","id":"individual-pk-profile-and-auc-0-24","dir":"Articles","previous_headings":"Intravenous ganciclovir","what":"Individual PK profile and AUC 0-24","title":"Route of administration","text":"individual PK profile can estimated, plotted.  difference cumulative AUC H144 H120 gives AUC 0-24 last dose. Using data.table optional, syntax convenient.","code":"map_patient <- poso_estim_map(patient,mod_ganciclovir_Caldes_2009) plot(map_patient$model,Cc) library(data.table) data.table(map_patient$model)[time==144,AUC] -    data.table(map_patient$model)[time==120,AUC] #> [1] 72.19085"},{"path":"https://levenc.github.io/posologyr/articles/route_of_administration.html","id":"optimal-dose-for-an-intravenous-ganciclovir-injection","dir":"Articles","previous_headings":"Intravenous ganciclovir","what":"Optimal dose for an intravenous ganciclovir injection","title":"Route of administration","text":"optimal dose achieve AUC 50 mg.h/L can determined new injection IV ganciclovir setting cmt_dose = \"centr\".","code":"poso_dose_auc(patient,mod_ganciclovir_Caldes_2009,tdm=TRUE,               time_dose = 145,               duration = 1,               time_auc = 24,               target_auc = 50,               cmt_dose = \"centr\") #> $dose #> [1] 156.5335 #>  #> $type_of_estimate #> [1] \"point estimate\" #>  #> $auc_estimate #> [1] 50 #>  #> $indiv_param #>   THETA_cl THETA_v1 THETA_cld THETA_v2 THETA_ka THETA_baf add.sd prop.sd #> 1     7.49     31.9      10.2       32    0.895     0.825  0.465   0.143 #>       ETA_cl     ETA_v1        ETA_ka       ETA_baf covar #> 1 0.05256541 -0.4773341 -3.589527e-08 -1.272466e-07    25"},{"path":"https://levenc.github.io/posologyr/articles/route_of_administration.html","id":"optimal-dose-for-an-oral-valganciclovir-administration","dir":"Articles","previous_headings":"Intravenous ganciclovir","what":"Optimal dose for an oral valganciclovir administration","title":"Route of administration","text":"optimal dose achieve AUC 50 mg.h/L can determined administration oral valganciclovir setting cmt_dose = \"depot\". Keeping default value cmt_dose, first compartment declared PK model, also work .","code":"poso_dose_auc(patient,mod_ganciclovir_Caldes_2009,tdm=TRUE,               time_dose = 145,               time_auc = 24,               target_auc = 50,               cmt_dose = \"depot\") #> $dose #> [1] 193.1298 #>  #> $type_of_estimate #> [1] \"point estimate\" #>  #> $auc_estimate #> [1] 50 #>  #> $indiv_param #>   THETA_cl THETA_v1 THETA_cld THETA_v2 THETA_ka THETA_baf add.sd prop.sd #> 1     7.49     31.9      10.2       32    0.895     0.825  0.465   0.143 #>      ETA_cl     ETA_v1        ETA_ka      ETA_baf covar #> 1 0.0525648 -0.4773328 -1.018546e-06 2.327066e-07    25"},{"path":"https://levenc.github.io/posologyr/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Cyril Leven. Author, maintainer, copyright holder. Matthew Fidler. Contributor. Emmanuelle Comets. Contributor. Audrey Lavenu. Contributor. Marc Lavielle. Contributor.","code":""},{"path":"https://levenc.github.io/posologyr/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Leven C, Coste , Mané C (2022). “Free Open-Source Posologyr Software Bayesian Dose Individualization: Extensive Validation Simulated Data.” Pharmaceutics, 14(2), 442. doi:10.3390/pharmaceutics14020442, https://europepmc.org/article/pmc/8879752.","code":"@Article{posologyrpaper,   title = {Free and Open-Source Posologyr Software for Bayesian Dose Individualization: An Extensive Validation on Simulated Data},   author = {Cyril Leven and Anne Coste and Camille Mané},   journal = {Pharmaceutics},   year = {2022},   volume = {14},   pages = {442},   number = {2},   doi = {10.3390/pharmaceutics14020442},   publisher = {MDPI},   url = {https://europepmc.org/article/pmc/8879752}, }"},{"path":[]},{"path":"https://levenc.github.io/posologyr/index.html","id":"overview","dir":"","previous_headings":"","what":"Overview","title":"Individual Dose Optimization using Population Pharmacokinetics","text":"Personalize drug regimens using individual pharmacokinetic (PK) pharmacokinetic-pharmacodynamic (PK-PD) profiles. combining therapeutic drug monitoring (TDM) data population model, posologyr offers accurate posterior estimates helps compute optimal individualized dosing regimens. Key dosage optimization functions posologyr include: poso_dose_conc() estimates optimal dose achieve target concentration given time poso_dose_auc() estimates dose needed reach target area concentration-time curve (AUC) poso_time_cmin() estimates time required reach target trough concentration (Cmin) poso_inter_cmin() estimates optimal dosing interval consistently achieve target Cmin Individual PK profiles can estimated without TDM data: poso_estim_map() computes Maximum Posteriori Bayesian Estimates (MAP-) individual PK parameters using TDM results poso_simu_pop() samples prior distributions PK parameters posologyr leverages simulation capabilities rxode2 package.","code":""},{"path":"https://levenc.github.io/posologyr/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Individual Dose Optimization using Population Pharmacokinetics","text":"can install released version posologyr CRAN : can install development version posologyr GitHub :","code":"install.packages(\"posologyr\") # install.packages(\"remotes\") remotes::install_github(\"levenc/posologyr\")"},{"path":"https://levenc.github.io/posologyr/index.html","id":"bayesian-dosing-example","dir":"","previous_headings":"","what":"Bayesian dosing example","title":"Individual Dose Optimization using Population Pharmacokinetics","text":"determine optimal dose gentamicin patient posologyr, need: prior PK model, written rxode2 mini-language example, gentamicin PK literature doi:10.1016/j.ijantimicag.2003.07.010 table patient’s TDM data, format similar data NONMEM","code":"mod_gentamicin_Xuan2003 <- function() {   ini({     THETA_Cl  = 0.047     THETA_V   = 0.28     THETA_k12 = 0.092     THETA_k21 = 0.071     ETA_Cl  ~ 0.084     ETA_V   ~ 0.003     ETA_k12 ~ 0.398     ETA_k21 ~ 0.342     add_sd  <- 0.230     prop_sd <- 0.237   })   model({     TVl   = THETA_Cl*ClCr     TVV   = THETA_V*WT     TVk12 = THETA_k12     TVk21 = THETA_k21          Cl    = TVl*exp(ETA_Cl)     V     = TVV*exp(ETA_V)     k12   = TVk12*exp(ETA_k12)     k21   = TVk21 *exp(ETA_k21)          ke    = Cl/V     Cp    = centr/V          d/dt(centr)  = - ke*centr - k12*centr + k21*periph     d/dt(periph) =            + k12*centr - k21*periph      Cp ~ add(add_sd) + prop(prop_sd) + combined1()   }) } patient_data <- data.frame(ID=1,                            TIME=c(0.0,1.0,11.0),                            DV=c(NA,9,2),                            AMT=c(180,0,0),                            DUR=c(0.5,NA,NA),                            EVID=c(1,0,0),                            ClCr=38,                            WT=63) patient_data #>   ID TIME DV AMT DUR EVID ClCr WT #> 1  1    0 NA 180 0.5    1   38 63 #> 2  1    1  9   0  NA    0   38 63 #> 3  1   11  2   0  NA    0   38 63"},{"path":"https://levenc.github.io/posologyr/index.html","id":"individual-pk-profile","dir":"","previous_headings":"Bayesian dosing example","what":"Individual PK profile","title":"Individual Dose Optimization using Population Pharmacokinetics","text":"two elements, can estimate plot individual concentrations time.","code":"library(\"posologyr\") patient_map <- poso_estim_map(patient_data,mod_gentamicin_Xuan2003) plot(patient_map$model,Cc)"},{"path":"https://levenc.github.io/posologyr/index.html","id":"dose-optimization","dir":"","previous_headings":"Bayesian dosing example","what":"Dose optimization","title":"Individual Dose Optimization using Population Pharmacokinetics","text":"optimize gentamicin dosage patient meet two criteria: peak concentration 12 mg/L, 30 minutes 30-minute infusion. trough concentration less 0.5 mg/L. time required reach residual concentration 0.5 mg/L can estimated follows: dose required achieve target concentration can determined infusion H48. conclusion dose 240 mg 48 h first injection appropriate meet 2 criteria. examples can found : https://levenc.github.io/posologyr/","code":"poso_time_cmin(patient_data,mod_gentamicin_Xuan2003,tdm=TRUE,                target_cmin = 0.5) #> $time #> [1] 44.9 #>  #> $type_of_estimate #> [1] \"point estimate\" #>  #> $cmin_estimate #> [1] 0.4991313 #>  #> $indiv_param #>   THETA_Cl THETA_V THETA_k12 THETA_k21 add_sd prop_sd     ETA_Cl       ETA_V #> 3    0.047    0.28     0.092     0.071   0.23   0.237 0.03701064 0.001447308 #>      ETA_k12     ETA_k21 ClCr WT #> 3 0.08904703 -0.04838898   38 63 poso_dose_conc(patient_data,mod_gentamicin_Xuan2003,tdm=TRUE,                target_conc = 12,duration=0.5,time_dose = 48,time_c = 49) #> $dose #> [1] 237.5902 #>  #> $type_of_estimate #> [1] \"point estimate\" #>  #> $conc_estimate #> [1] 12 #>  #> $indiv_param #>   THETA_Cl THETA_V THETA_k12 THETA_k21 add_sd prop_sd     ETA_Cl       ETA_V #> 3    0.047    0.28     0.092     0.071   0.23   0.237 0.03701052 0.001447305 #>      ETA_k12     ETA_k21 ClCr WT #> 3 0.08904752 -0.04838936   38 63"},{"path":"https://levenc.github.io/posologyr/index.html","id":"performance-of-the-map-be-algorithm-in-posologyr","dir":"","previous_headings":"","what":"Performance of the MAP-BE algorithm in posologyr","title":"Individual Dose Optimization using Population Pharmacokinetics","text":"posologyr showed comparable performance NONMEM MAP estimation option MAXEVAL=0: Pharmaceutics 2022, 14(2), 442; doi:10.3390/pharmaceutics14020442 Supporting data: https://github.com/levenc/posologyr-pharmaceutics","code":""},{"path":"https://levenc.github.io/posologyr/reference/error_model_comb1.html","id":null,"dir":"Reference","previous_headings":"","what":"Residual error model combined 1 — error_model_comb1","title":"Residual error model combined 1 — error_model_comb1","text":"Residual error model combined 1. Constant error model proportional coefficient provided. Proportional error model constant (additive) error coefficient provided.","code":""},{"path":"https://levenc.github.io/posologyr/reference/error_model_comb1.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Residual error model combined 1 — error_model_comb1","text":"","code":"error_model_comb1(f, sigma)"},{"path":"https://levenc.github.io/posologyr/reference/error_model_comb1.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Residual error model combined 1 — error_model_comb1","text":"f Numeric vector, output pharmacokinetic model sigma Numeric vector coefficients residual error model","code":""},{"path":"https://levenc.github.io/posologyr/reference/error_model_comb1.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Residual error model combined 1 — error_model_comb1","text":"Numeric vector, residual error","code":""},{"path":"https://levenc.github.io/posologyr/reference/error_model_comb1.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Residual error model combined 1 — error_model_comb1","text":"Implements following function: g <- sigma[1] + sigma[2]*f","code":""},{"path":"https://levenc.github.io/posologyr/reference/error_model_comb2.html","id":null,"dir":"Reference","previous_headings":"","what":"Residual error model combined 2 — error_model_comb2","title":"Residual error model combined 2 — error_model_comb2","text":"Residual error model combined 2.","code":""},{"path":"https://levenc.github.io/posologyr/reference/error_model_comb2.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Residual error model combined 2 — error_model_comb2","text":"","code":"error_model_comb2(f, sigma)"},{"path":"https://levenc.github.io/posologyr/reference/error_model_comb2.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Residual error model combined 2 — error_model_comb2","text":"f Numeric vector, output pharmacokinetic model sigma Numeric vector coefficients residual error model","code":""},{"path":"https://levenc.github.io/posologyr/reference/error_model_comb2.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Residual error model combined 2 — error_model_comb2","text":"Numeric vector, residual error","code":""},{"path":"https://levenc.github.io/posologyr/reference/error_model_comb2.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Residual error model combined 2 — error_model_comb2","text":"Implements following function: g <- sqrt(sigma[1]^2 + sigma[2]^2*f^2)","code":""},{"path":"https://levenc.github.io/posologyr/reference/error_model_mixednm.html","id":null,"dir":"Reference","previous_headings":"","what":"Residual error model mixed (idem NONMEM) — error_model_mixednm","title":"Residual error model mixed (idem NONMEM) — error_model_mixednm","text":"Mixed residual error model, similar NONMEM implementation.","code":""},{"path":"https://levenc.github.io/posologyr/reference/error_model_mixednm.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Residual error model mixed (idem NONMEM) — error_model_mixednm","text":"","code":"error_model_mixednm(f, sigma)"},{"path":"https://levenc.github.io/posologyr/reference/error_model_mixednm.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Residual error model mixed (idem NONMEM) — error_model_mixednm","text":"f Numeric vector, output pharmacokinetic model sigma Matrix coefficients residual error model","code":""},{"path":"https://levenc.github.io/posologyr/reference/error_model_mixednm.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Residual error model mixed (idem NONMEM) — error_model_mixednm","text":"Numeric vector, residual error","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_dose_auc.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the dose needed to reach a target area under the concentration-time curve (AUC) — poso_dose_auc","title":"Estimate the dose needed to reach a target area under the concentration-time curve (AUC) — poso_dose_auc","text":"estimates dose needed reach target area concentration-time curve (AUC) given population pharmacokinetic model, set individual parameters, target AUC.","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_dose_auc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the dose needed to reach a target area under the concentration-time curve (AUC) — poso_dose_auc","text":"","code":"poso_dose_auc(   dat = NULL,   prior_model = NULL,   tdm = FALSE,   time_auc,   time_dose = NULL,   cmt_dose = 1,   target_auc,   estim_method = \"map\",   nocb = FALSE,   p = NULL,   greater_than = TRUE,   starting_time = 0,   interdose_interval = NULL,   add_dose = NULL,   duration = 0,   starting_dose = 100,   indiv_param = NULL )"},{"path":"https://levenc.github.io/posologyr/reference/poso_dose_auc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the dose needed to reach a target area under the concentration-time curve (AUC) — poso_dose_auc","text":"dat Dataframe. individual subject dataset following structure NONMEM/rxode2 event records. prior_model posologyr prior population pharmacokinetics model, list six objects. tdm boolean. TRUE: estimates optimal dose selected target auc selected duration following events dat, using Maximum Posteriori estimation. Setting tdm TRUE causes following occur: time_dose argument required used starting point AUC calculation instead starting_time; arguments estim_method, p, greater_than, interdose_interval, add_dose, indiv_param starting_time ignored. time_auc Numeric. duration. target AUC computed starting_time starting_time + time_auc. tdm set TRUE target AUC computed time_dose time_dose + time_auc instead. time_dose Numeric. Time dose given. used mandatory, tdm set TRUE. cmt_dose Character numeric. compartment dose administered. Must match one compartments prior model. Defaults 1. target_auc Numeric. target AUC. estim_method character string. estimation method used individual parameters. default method \"map\" Maximum Posteriori estimation, method \"prior\" simulates prior population model, \"sir\" uses Sequential Importance Resampling algorithm estimate posteriori distribution individual parameters. argument ignored indiv_param provided, tdm set TRUE. nocb boolean. time-varying covariates: next observation carried backward (nocb) interpolation style, similar NONMEM.  FALSE, last observation carried forward (locf) style used. Defaults FALSE. p Numeric. proportion distribution AUC consider optimization. Mandatory estim_method=sir. argument ignored tdm set TRUE. greater_than boolean. TRUE: targets dose leading proportion p AUCs greater target_auc. Respectively, lower FALSE. argument ignored tdm set TRUE. starting_time Numeric. First point time AUC, multiple dose regimen. default zero. argument ignored tdm set TRUE, time_dose used starting point instead. interdose_interval Numeric. Time interdose interval multiple dose regimen. Must provided add_dose used. argument ignored tdm set TRUE. add_dose Numeric. Additional doses administered inter-dose interval first dose. Optional. argument ignored tdm set TRUE. duration Numeric. Duration infusion, zero-order administrations. starting_dose Numeric. Starting dose optimization algorithm. indiv_param Optional. set individual parameters : THETA, estimates ETA, covariates. argument ignored tdm set TRUE.","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_dose_auc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the dose needed to reach a target area under the concentration-time curve (AUC) — poso_dose_auc","text":"list containing following components: dose Numeric. optimal dose selected target AUC. type_of_estimate Character string. type estimate individual parameters. Either point estimate, distribution. auc_estimate vector numeric estimates AUC. Either single value (point estimate ETA), distribution. indiv_param data.frame. set individual parameters used determination optimal dose : THETA, estimates ETA, covariates","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_dose_auc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate the dose needed to reach a target area under the concentration-time curve (AUC) — poso_dose_auc","text":"","code":"rxode2::setRxThreads(2L) # limit the number of threads  # model mod_run001 <- function() {   ini({     THETA_Cl <- 4.0     THETA_Vc <- 70.0     THETA_Ka <- 1.0     ETA_Cl ~ 0.2     ETA_Vc ~ 0.2     ETA_Ka ~ 0.2     prop.sd <- sqrt(0.05)   })   model({     TVCl <- THETA_Cl     TVVc <- THETA_Vc     TVKa <- THETA_Ka      Cl <- TVCl*exp(ETA_Cl)     Vc <- TVVc*exp(ETA_Vc)     Ka <- TVKa*exp(ETA_Ka)      K20 <- Cl/Vc     Cc <- centr/Vc      d/dt(depot) = -Ka*depot     d/dt(centr) = Ka*depot - K20*centr     Cc ~ prop(prop.sd)   }) } # df_patient01: event table for Patient01, following a 30 minutes intravenous # infusion df_patient01 <- data.frame(ID=1,                         TIME=c(0.0,1.0,14.0),                         DV=c(NA,25.0,5.5),                         AMT=c(2000,0,0),                         EVID=c(1,0,0),                         DUR=c(0.5,NA,NA)) # estimate the optimal dose to reach an AUC(0-12h) of 45 h.mg/l poso_dose_auc(dat=df_patient01,prior_model=mod_run001, time_auc=12,target_auc=45) #>   #>   #>   #>   #> $dose #> [1] 396.0027 #>  #> $type_of_estimate #> [1] \"point estimate\" #>  #> $auc_estimate #> [1] 45 #>  #> $indiv_param #>   THETA_Cl THETA_Vc THETA_Ka   prop.sd    ETA_Cl     ETA_Vc    ETA_Ka #> 1        4       70        1 0.2236068 0.6018995 -0.4291782 0.1278321 #>"},{"path":"https://levenc.github.io/posologyr/reference/poso_dose_conc.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the optimal dose to achieve a target concentration at any given time — poso_dose_conc","title":"Estimate the optimal dose to achieve a target concentration at any given time — poso_dose_conc","text":"Estimates optimal dose achieve target concentration given time given population pharmacokinetic model, set individual parameters, selected point time, target concentration.","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_dose_conc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the optimal dose to achieve a target concentration at any given time — poso_dose_conc","text":"","code":"poso_dose_conc(   dat = NULL,   prior_model = NULL,   tdm = FALSE,   time_c,   time_dose = NULL,   target_conc,   cmt_dose = 1,   endpoint = \"Cc\",   estim_method = \"map\",   nocb = FALSE,   p = NULL,   greater_than = TRUE,   starting_dose = 100,   interdose_interval = NULL,   add_dose = NULL,   duration = 0,   indiv_param = NULL )"},{"path":"https://levenc.github.io/posologyr/reference/poso_dose_conc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the optimal dose to achieve a target concentration at any given time — poso_dose_conc","text":"dat Dataframe. individual subject dataset following structure NONMEM/rxode2 event records. prior_model posologyr prior population pharmacokinetics model, list six objects. tdm boolean. TRUE: estimates optimal dose selected target concentration selected point time following events dat, using Maximum Posteriori estimation. Setting tdm TRUE causes following occur: arguments estim_method, p, greater_than, interdose_interval, add_dose, indiv_param starting_time ignored. time_c Numeric. Point time dose optimized. time_dose Numeric. Time dose given. target_conc Numeric. Target concentration. cmt_dose Character numeric. compartment dose administered. Must match one compartments prior model. Defaults 1. endpoint Character. endpoint prior model optimised . default \"Cc\", central concentration. estim_method character string. estimation method used individual parameters. default method \"map\" Maximum Posteriori estimation, method \"prior\" simulates prior population model, \"sir\" uses Sequential Importance Resampling algorithm estimate posteriori distribution individual parameters. argument ignored indiv_param provided tdm set TRUE. nocb boolean. time-varying covariates: next observation carried backward (nocb) interpolation style, similar NONMEM.  FALSE, last observation carried forward (locf) style used. Defaults FALSE. p Numeric. proportion distribution concentrations consider optimization. Mandatory estim_method=sir. argument ignored tdm set TRUE. greater_than boolean. TRUE: targets dose leading proportion p concentrations greater target_conc. Respectively, lower FALSE. argument ignored tdm set TRUE. starting_dose Numeric. Starting dose optimization algorithm. interdose_interval Numeric. Time interdose interval multiple dose regimen. Must provided add_dose used. argument ignored tdm set TRUE. add_dose Numeric. Additional doses administered inter-dose interval first dose. Optional. argument ignored tdm set TRUE. duration Numeric. Duration infusion, zero-order administrations. indiv_param Optional. set individual parameters : THETA, estimates ETA, covariates. argument ignored tdm set TRUE.","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_dose_conc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the optimal dose to achieve a target concentration at any given time — poso_dose_conc","text":"list containing following components: dose Numeric. optimal dose selected target concentration. type_of_estimate Character string. type estimate individual parameters. Either point estimate, distribution. conc_estimate vector numeric estimates conc. Either single value (point estimate ETA), distribution. indiv_param data.frame. set individual parameters used determination optimal dose : THETA, estimates ETA, covariates","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_dose_conc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate the optimal dose to achieve a target concentration at any given time — poso_dose_conc","text":"","code":"rxode2::setRxThreads(2L) # limit the number of threads  # model mod_run001 <- function() {   ini({     THETA_Cl <- 4.0     THETA_Vc <- 70.0     THETA_Ka <- 1.0     ETA_Cl ~ 0.2     ETA_Vc ~ 0.2     ETA_Ka ~ 0.2     prop.sd <- sqrt(0.05)   })   model({     TVCl <- THETA_Cl     TVVc <- THETA_Vc     TVKa <- THETA_Ka      Cl <- TVCl*exp(ETA_Cl)     Vc <- TVVc*exp(ETA_Vc)     Ka <- TVKa*exp(ETA_Ka)      K20 <- Cl/Vc     Cc <- centr/Vc      d/dt(depot) = -Ka*depot     d/dt(centr) = Ka*depot - K20*centr     Cc ~ prop(prop.sd)   }) } # df_patient01: event table for Patient01, following a 30 minutes intravenous # infusion df_patient01 <- data.frame(ID=1,                         TIME=c(0.0,1.0,14.0),                         DV=c(NA,25.0,5.5),                         AMT=c(2000,0,0),                         EVID=c(1,0,0),                         DUR=c(0.5,NA,NA)) # estimate the optimal dose to reach a concentration of 80 mg/l # one hour after starting the 30-minutes infusion poso_dose_conc(dat=df_patient01,prior_model=mod_run001, time_c=1,duration=0.5,target_conc=80) #>   #>   #>   #>   #> $dose #> [1] 6886.024 #>  #> $type_of_estimate #> [1] \"point estimate\" #>  #> $conc_estimate #> [1] 80 #>  #> $indiv_param #>   THETA_Cl THETA_Vc THETA_Ka   prop.sd    ETA_Cl     ETA_Vc    ETA_Ka #> 1        4       70        1 0.2236068 0.6019041 -0.4291723 0.1278484 #>"},{"path":"https://levenc.github.io/posologyr/reference/poso_estim_map.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the Maximum A Posteriori individual parameters — poso_estim_map","title":"Estimate the Maximum A Posteriori individual parameters — poso_estim_map","text":"Estimates Maximum Posteriori (MAP) individual parameters, also known Empirical Bayes Estimates (EBE).","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_estim_map.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the Maximum A Posteriori individual parameters — poso_estim_map","text":"","code":"poso_estim_map(   dat = NULL,   prior_model = NULL,   return_model = TRUE,   return_ofv = FALSE,   nocb = FALSE )"},{"path":"https://levenc.github.io/posologyr/reference/poso_estim_map.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the Maximum A Posteriori individual parameters — poso_estim_map","text":"dat Dataframe. individual subject dataset following structure NONMEM/rxode2 event records. prior_model posologyr prior population pharmacokinetics model, list six objects. return_model boolean. Returns rxode2 model using estimated ETAs set TRUE. return_ofv boolean. Returns Objective Function Value (OFV) set TRUE. nocb boolean. time-varying covariates: next observation carried backward (nocb) interpolation style, similar NONMEM.  FALSE, last observation carried forward (locf) style used. Defaults FALSE.","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_estim_map.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the Maximum A Posteriori individual parameters — poso_estim_map","text":"named list consisting one following elements depending input parameters function: $eta named vector MAP estimates individual values ETA, $model rxode2 model using estimated ETAs, $event data.table used solve returned rxode2 model.","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_estim_map.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate the Maximum A Posteriori individual parameters — poso_estim_map","text":"","code":"rxode2::setRxThreads(1) # limit the number of threads  # model mod_run001 <- function() {   ini({     THETA_Cl <- 4.0     THETA_Vc <- 70.0     THETA_Ka <- 1.0     ETA_Cl ~ 0.2     ETA_Vc ~ 0.2     ETA_Ka ~ 0.2     prop.sd <- sqrt(0.05)   })   model({     TVCl <- THETA_Cl     TVVc <- THETA_Vc     TVKa <- THETA_Ka      Cl <- TVCl*exp(ETA_Cl)     Vc <- TVVc*exp(ETA_Vc)     Ka <- TVKa*exp(ETA_Ka)      K20 <- Cl/Vc     Cc <- centr/Vc      d/dt(depot) = -Ka*depot     d/dt(centr) = Ka*depot - K20*centr     Cc ~ prop(prop.sd)   }) } # df_patient01: event table for Patient01, following a 30 minutes intravenous # infusion df_patient01 <- data.frame(ID=1,                         TIME=c(0.0,1.0,14.0),                         DV=c(NA,25.0,5.5),                         AMT=c(2000,0,0),                         EVID=c(1,0,0),                         DUR=c(0.5,NA,NA)) # estimate the Maximum A Posteriori individual parameters poso_estim_map(dat=df_patient01,prior_model=mod_run001) #>   #>   #>   #>   #> $eta #>     ETA_Cl     ETA_Vc     ETA_Ka  #>  0.6019038 -0.4291730  0.1278482  #>  #> $model #> ── Solved rxode2 object ── #> ── Parameters ($params): ── #>   THETA_Cl   THETA_Vc   THETA_Ka    prop.sd     ETA_Cl     ETA_Vc     ETA_Ka  #>  4.0000000 70.0000000  1.0000000  0.2236068  0.6019038 -0.4291730  0.1278482  #> ── Initial Conditions ($inits): ── #> depot centr   AUC  #>     0     0     0  #> ── First part of data (object): ── #> # A tibble: 151 × 13 #>    time  TVCl  TVVc  TVKa    Cl    Vc    Ka   K20   rxCc     Cc depot centr #>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>  <dbl>  <dbl> <dbl> <dbl> #> 1   0       4    70     1  7.30  45.6  1.14 0.160  0      0        0    0   #> 2   0.1     4    70     1  7.30  45.6  1.14 0.160  0.478  0.478  378.  21.8 #> 3   0.2     4    70     1  7.30  45.6  1.14 0.160  1.83   1.83   716.  83.5 #> 4   0.3     4    70     1  7.30  45.6  1.14 0.160  3.95   3.95  1017. 180.  #> 5   0.4     4    70     1  7.30  45.6  1.14 0.160  6.75   6.75  1286. 307.  #> 6   0.5     4    70     1  7.30  45.6  1.14 0.160 10.1   10.1   1526. 461.  #> # ℹ 145 more rows #> # ℹ 1 more variable: AUC <dbl> #>  #> $event #>         id  time   amt  evid   dur #>      <int> <num> <num> <int> <num> #>   1:     1   0.0    NA     0    NA #>   2:     1   0.0  2000     1   0.5 #>   3:     1   0.1    NA     0    NA #>   4:     1   0.2    NA     0    NA #>   5:     1   0.3    NA     0    NA #>  ---                               #> 148:     1  14.6    NA     0    NA #> 149:     1  14.7    NA     0    NA #> 150:     1  14.8    NA     0    NA #> 151:     1  14.9    NA     0    NA #> 152:     1  15.0    NA     0    NA #>"},{"path":"https://levenc.github.io/posologyr/reference/poso_estim_mcmc.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the posterior distribution of individual parameters by MCMC — poso_estim_mcmc","title":"Estimate the posterior distribution of individual parameters by MCMC — poso_estim_mcmc","text":"Estimates posterior distribution individual parameters Markov Chain Monte Carlo (using Metropolis-Hastings algorithm)","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_estim_mcmc.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the posterior distribution of individual parameters by MCMC — poso_estim_mcmc","text":"","code":"poso_estim_mcmc(   dat = NULL,   prior_model = NULL,   return_model = TRUE,   burn_in = 50,   n_iter = 1000,   n_chains = 4,   nocb = FALSE,   control = list(n_kernel = c(2, 2, 2), stepsize_rw = 0.4, proba_mcmc = 0.3, nb_max = 3) )"},{"path":"https://levenc.github.io/posologyr/reference/poso_estim_mcmc.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the posterior distribution of individual parameters by MCMC — poso_estim_mcmc","text":"dat Dataframe. individual subject dataset following structure NONMEM/rxode2 event records. prior_model posologyr prior population pharmacokinetics model, list six objects. return_model boolean. Returns rxode2 model using estimated ETAs set TRUE. burn_in Number burn-iterations Metropolis-Hastings algorithm. n_iter Total number iterations (following burn-iterations) Markov chain Metropolis-Hastings algorithm. n_chains Number Markov chains nocb boolean. time-varying covariates: next observation carried backward (nocb) interpolation style, similar NONMEM.  FALSE, last observation carried forward (locf) style used. Defaults FALSE. control list parameters controlling Metropolis-Hastings algorithm.","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_estim_mcmc.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the posterior distribution of individual parameters by MCMC — poso_estim_mcmc","text":"return_model set FALSE, list one element: dataframe $eta ETAs posterior distribution, estimated Markov Chain Monte Carlo. return_model set TRUE, list dataframe posterior distribution ETA, rxode2 model using estimated distributions ETAs.","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_estim_mcmc.html","id":"references","dir":"Reference","previous_headings":"","what":"References","title":"Estimate the posterior distribution of individual parameters by MCMC — poso_estim_mcmc","text":"Comets  E, Lavenu , Lavielle M. Parameter estimation nonlinear mixed effect models using saemix, R implementation SAEM algorithm. Journal Statistical Software 80, 3 (2017), 1-41.","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_estim_mcmc.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"Estimate the posterior distribution of individual parameters by MCMC — poso_estim_mcmc","text":"Emmanuelle Comets, Audrey Lavenu, Marc Lavielle, Cyril Leven","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_estim_mcmc.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate the posterior distribution of individual parameters by MCMC — poso_estim_mcmc","text":"","code":"# model mod_run001 <- function() {   ini({     THETA_Cl <- 4.0     THETA_Vc <- 70.0     THETA_Ka <- 1.0     ETA_Cl ~ 0.2     ETA_Vc ~ 0.2     ETA_Ka ~ 0.2     prop.sd <- sqrt(0.05)   })   model({     TVCl <- THETA_Cl     TVVc <- THETA_Vc     TVKa <- THETA_Ka      Cl <- TVCl*exp(ETA_Cl)     Vc <- TVVc*exp(ETA_Vc)     Ka <- TVKa*exp(ETA_Ka)      K20 <- Cl/Vc     Cc <- centr/Vc      d/dt(depot) = -Ka*depot     d/dt(centr) = Ka*depot - K20*centr     Cc ~ prop(prop.sd)   }) } # df_patient01: event table for Patient01, following a 30 minutes intravenous # infusion df_patient01 <- data.frame(ID=1,                         TIME=c(0.0,1.0,14.0),                         DV=c(NA,25.0,5.5),                         AMT=c(2000,0,0),                         EVID=c(1,0,0),                         DUR=c(0.5,NA,NA)) # estimate the posterior distribution of population parameters poso_estim_mcmc(dat=df_patient01,prior_model=mod_run001, n_iter=50,n_chains=2) #>   #>   #>   #>   #> $eta #>          ETA_Cl       ETA_Vc       ETA_Ka #> 1    0.40129734 -0.221078172  0.930425874 #> 2    0.46219626 -0.408861774  0.919479583 #> 3    0.46733199 -0.303257501  0.231586070 #> 4    0.70605689 -0.389072743  0.231586070 #> 5    0.71226862 -0.460682597  0.220690179 #> 6    0.45848168 -0.156826054  0.655923830 #> 7    0.71578116 -0.356073917  0.443066290 #> 8    0.39429314 -0.250227766  0.029447638 #> 9    0.79934008 -0.300123505  0.029447638 #> 10   0.64891523 -0.602467643 -0.257596645 #> 11   0.68534557 -0.393584606  0.471011853 #> 12   0.41497705 -0.469426296  0.200708751 #> 13  -0.28560298 -0.336731063  0.574758130 #> 14   0.69972327 -0.384404332 -0.094838457 #> 15   0.56697115 -0.427648209 -0.154894296 #> 16   0.52583412 -0.193851708  0.029564358 #> 17   0.73647568 -0.145748104  0.240492264 #> 18   0.72223572 -0.174057317  0.765403502 #> 19   0.61003356 -0.349027845  0.290769918 #> 20   0.69904470 -0.047834356  0.422258849 #> 21   0.43859699 -0.612412417 -0.098503394 #> 22   0.69096316 -0.051944909  0.914823122 #> 23   0.54729672  0.005972018  0.337291445 #> 24   0.27761493 -0.237833012  0.271761486 #> 25   0.64326906  0.011240352  0.623424475 #> 26   0.61126409 -0.364932526 -0.290915542 #> 27   0.36850878 -0.351410700  0.388243848 #> 28   0.27661006 -0.581498663 -0.230915305 #> 29  -0.23732985 -0.526465862  0.407033222 #> 30   0.19616325 -0.645638552 -0.494479393 #> 31   0.47330453 -0.034944362  0.470257171 #> 32   0.33639400 -0.515573239 -0.050276895 #> 33   0.78830319 -0.387354276 -0.031447965 #> 34   0.48064119 -0.343826881  0.244052299 #> 35   0.60189998  0.034441740  0.469481783 #> 36   0.77837261 -0.215748962  0.185718893 #> 37   0.75914818 -0.176417195  0.302207225 #> 38   0.68090244 -0.346820687  0.036191074 #> 39   0.54490321 -0.629963624  0.473465262 #> 40   0.49828937 -0.283363262  0.356249587 #> 41   0.54257518 -0.494014971  0.374892911 #> 42   0.62410466 -0.260718197  0.409061732 #> 43   0.15855684 -0.878222982 -0.131889899 #> 44   0.75140183 -0.141857343  0.326987451 #> 45   0.07179731 -0.032165060  0.268610671 #> 46   0.41210387 -0.258656564  0.462389655 #> 47   0.74462060 -0.382517639  0.673070802 #> 48   0.78287745 -0.073199344  0.521618599 #> 49   0.73952962  0.231836592  0.907839064 #> 50   0.53905726 -0.042012025  0.483275077 #> 51   0.00000000  0.000000000  0.000000000 #> 52   0.42534184 -0.403708105  1.056572451 #> 53   0.55618191 -0.264477715  0.046032939 #> 54   0.45520633 -0.679464662 -0.016242431 #> 55   0.44009565 -0.409873762  0.340896578 #> 56   0.57429692 -0.387726602  0.613547280 #> 57   0.54488941 -0.287829000  0.140149483 #> 58   0.48402318 -0.798975605  0.161344579 #> 59   0.56660040 -0.479785996  0.138614462 #> 60   0.61758410 -0.297867848  0.278458135 #> 61   0.73780864  0.023043168  0.551507628 #> 62   0.68002525 -0.319894513  0.234239231 #> 63   0.89503107 -0.037166313  0.600292939 #> 64   0.78780453 -0.263742002  0.175122402 #> 65   0.51360658  0.052946634  0.892954576 #> 66   0.64420451 -0.318996298  0.650988522 #> 67   0.68813422 -0.251065357  0.415733557 #> 68   0.78325406  0.094584973  0.439275927 #> 69   0.48416155 -0.480703520  0.124244451 #> 70   0.48645250 -0.663751410  0.007279203 #> 71   0.42371624 -0.511424673  0.239185177 #> 72   0.79998492 -0.327110133 -0.287008745 #> 73   0.55606168 -0.300343887 -0.103813643 #> 74  -0.18397797 -0.957175830  0.033660836 #> 75   0.33043554 -0.828096146 -0.054257139 #> 76   0.18819311 -0.839629729  0.143333722 #> 77   0.08262417 -0.414383051 -0.225163689 #> 78   0.37230513 -0.375817501  0.666112298 #> 79   0.51699274 -0.934285571 -0.299695700 #> 80   0.08783468 -0.800477076 -0.147109306 #> 81   0.68133590 -0.368815872  0.040840687 #> 82   0.65031284 -0.323466120  0.398707403 #> 83   0.52147855 -0.586734782 -0.127577976 #> 84   0.71718247 -0.171249178  0.372917693 #> 85   0.52524631 -0.377795316  0.640937425 #> 86   0.80589852  0.156365752  0.993932092 #> 87   0.43878864 -0.746336825  0.356184103 #> 88   0.13803478 -0.812773685  0.083051161 #> 89   0.49241332 -0.249973773  0.507466133 #> 90   0.81523875 -0.116686814  0.532386442 #> 91   0.28613945 -0.434134521 -0.055910865 #> 92   0.16400899 -0.611578714 -0.265540778 #> 93   0.49782289 -0.500626081  0.300302428 #> 94   0.69449874 -0.069173401  0.542000876 #> 95   0.44901310 -0.630662806 -0.542320029 #> 96   0.39487244  0.142613375  1.402640547 #> 97   0.64429477 -0.461460431  0.136638918 #> 98   0.57768550 -0.319792195  0.097921632 #> 99   0.56412639 -0.496782084 -0.041628981 #> 100  0.32575969 -0.106694594  0.762696118 #>  #> $model #> ── Solved rxode2 object ── #> ── Parameters ($params): ── #> # A tibble: 100 × 8 #>    sim.id THETA_Cl THETA_Vc THETA_Ka prop.sd ETA_Cl ETA_Vc  ETA_Ka #>     <int>    <dbl>    <dbl>    <dbl>   <dbl>  <dbl>  <dbl>   <dbl> #>  1      1        4       70        1   0.224  0.401 -0.221  0.930  #>  2      2        4       70        1   0.224  0.462 -0.409  0.919  #>  3      3        4       70        1   0.224  0.467 -0.303  0.232  #>  4      4        4       70        1   0.224  0.706 -0.389  0.232  #>  5      5        4       70        1   0.224  0.712 -0.461  0.221  #>  6      6        4       70        1   0.224  0.458 -0.157  0.656  #>  7      7        4       70        1   0.224  0.716 -0.356  0.443  #>  8      8        4       70        1   0.224  0.394 -0.250  0.0294 #>  9      9        4       70        1   0.224  0.799 -0.300  0.0294 #> 10     10        4       70        1   0.224  0.649 -0.602 -0.258  #> # ℹ 90 more rows #> ── Initial Conditions ($inits): ── #> depot centr   AUC  #>     0     0     0  #>  #> Simulation without uncertainty in parameters, omega, or sigma matricies #>  #> ── First part of data (object): ── #> # A tibble: 200 × 14 #>   sim.id  time  TVCl  TVVc  TVKa    Cl    Vc    Ka   K20  rxCc    Cc    depot #>    <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>    <dbl> #> 1      1     1     4    70     1  5.98  56.1  2.54 0.106 28.4  28.4   3.19e+2 #> 2      1    14     4    70     1  5.98  56.1  2.54 0.106  8.61  8.61  7.99e-9 #> 3      2     1     4    70     1  6.35  46.5  2.51 0.137 33.7  33.7   3.25e+2 #> 4      2    14     4    70     1  6.35  46.5  2.51 0.137  6.96  6.96 -2.81e-9 #> 5      3     1     4    70     1  6.38  51.7  1.26 0.123 22.2  22.2   7.90e+2 #> 6      3    14     4    70     1  6.38  51.7  1.26 0.123  7.85  7.85  6.03e-5 #> # ℹ 194 more rows #> # ℹ 2 more variables: centr <dbl>, AUC <dbl> #>"},{"path":"https://levenc.github.io/posologyr/reference/poso_estim_sir.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the posterior distribution of individual parameters by SIR — poso_estim_sir","title":"Estimate the posterior distribution of individual parameters by SIR — poso_estim_sir","text":"Estimates posterior distribution individual parameters Sequential Importance Resampling (SIR)","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_estim_sir.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the posterior distribution of individual parameters by SIR — poso_estim_sir","text":"","code":"poso_estim_sir(   dat = NULL,   prior_model = NULL,   n_sample = 10000,   n_resample = 1000,   return_model = TRUE,   nocb = FALSE )"},{"path":"https://levenc.github.io/posologyr/reference/poso_estim_sir.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the posterior distribution of individual parameters by SIR — poso_estim_sir","text":"dat Dataframe. individual subject dataset following structure NONMEM/rxode2 event records. prior_model posologyr prior population pharmacokinetics model, list six objects. n_sample Number samples S-step n_resample Number samples R-step return_model boolean. Returns rxode2 model using estimated ETAs set TRUE. nocb boolean. time-varying covariates: next observation carried backward (nocb) interpolation style, similar NONMEM.  FALSE, last observation carried forward (locf) style used. Defaults FALSE.","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_estim_sir.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the posterior distribution of individual parameters by SIR — poso_estim_sir","text":"return_model set FALSE, list one element: dataframe $eta ETAs posterior distribution, estimated Sequential Importance Resampling. return_model set TRUE, list dataframe posterior distribution ETA, rxode2 model using estimated distributions ETAs.","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_estim_sir.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate the posterior distribution of individual parameters by SIR — poso_estim_sir","text":"","code":"# model mod_run001 <- function() {   ini({     THETA_Cl <- 4.0     THETA_Vc <- 70.0     THETA_Ka <- 1.0     ETA_Cl ~ 0.2     ETA_Vc ~ 0.2     ETA_Ka ~ 0.2     prop.sd <- sqrt(0.05)   })   model({     TVCl <- THETA_Cl     TVVc <- THETA_Vc     TVKa <- THETA_Ka      Cl <- TVCl*exp(ETA_Cl)     Vc <- TVVc*exp(ETA_Vc)     Ka <- TVKa*exp(ETA_Ka)      K20 <- Cl/Vc     Cc <- centr/Vc      d/dt(depot) = -Ka*depot     d/dt(centr) = Ka*depot - K20*centr     Cc ~ prop(prop.sd)   }) } # df_patient01: event table for Patient01, following a 30 minutes intravenous # infusion df_patient01 <- data.frame(ID=1,                         TIME=c(0.0,1.0,14.0),                         DV=c(NA,25.0,5.5),                         AMT=c(2000,0,0),                         EVID=c(1,0,0),                         DUR=c(0.5,NA,NA)) # estimate the posterior distribution of population parameters poso_estim_sir(dat=df_patient01,prior_model=mod_run001, n_sample=1e3,n_resample=1e2) #>   #>   #>   #>   #> $eta #>        ETA_Cl      ETA_Vc      ETA_Ka #> 1   0.6504014 -0.46513521  0.43653693 #> 2   0.7998906 -0.03341945  0.16662057 #> 3   0.5739246 -0.05601440  0.71549851 #> 4   0.7089199 -0.17563653  0.37654151 #> 5   0.6224641 -0.44103680  0.11062341 #> 6   0.5756166 -0.54744862 -0.11639086 #> 7   0.2097388 -0.80535811 -0.39535473 #> 8   0.4410245 -0.51368224  0.18837049 #> 9   0.8041649 -0.20072306  0.23829165 #> 10  0.3270809 -0.20504934  0.30219971 #> 11  0.6224641 -0.44103680  0.11062341 #> 12  0.6901039 -0.30108197 -0.14402225 #> 13  0.5756166 -0.54744862 -0.11639086 #> 14  0.6224641 -0.44103680  0.11062341 #> 15  0.7089199 -0.17563653  0.37654151 #> 16  0.7740971 -0.30891172  0.57370521 #> 17  0.6504014 -0.46513521  0.43653693 #> 18  0.6280538 -0.23392662  0.44682453 #> 19  0.7089199 -0.17563653  0.37654151 #> 20  0.3976482 -0.64529135  0.23007851 #> 21  0.3425794 -0.70136613  0.12968425 #> 22  0.5716248 -0.36818884  0.36830570 #> 23  0.2097388 -0.80535811 -0.39535473 #> 24  0.8530497 -0.23492316  0.25581125 #> 25  0.6354166 -0.31357126  0.73866202 #> 26  0.6280538 -0.23392662  0.44682453 #> 27  0.5756166 -0.54744862 -0.11639086 #> 28  0.8530497 -0.23492316  0.25581125 #> 29  0.6354166 -0.31357126  0.73866202 #> 30  0.5480420 -0.78238693 -0.54016365 #> 31  0.6504014 -0.46513521  0.43653693 #> 32  0.6956159 -0.25114851  0.92671472 #> 33  0.3653215 -0.46129773  0.18425818 #> 34  0.4621170 -0.71068616  0.26608879 #> 35  0.2319779 -0.86032500 -0.48170475 #> 36  0.6956159 -0.25114851  0.92671472 #> 37  0.3313779 -0.54665431  0.48542548 #> 38  0.7089199 -0.17563653  0.37654151 #> 39  0.6947877 -0.46349506  0.19509695 #> 40  0.4179024 -0.58931306  0.22519506 #> 41  0.6224641 -0.44103680  0.11062341 #> 42  0.5480420 -0.78238693 -0.54016365 #> 43  0.3653215 -0.46129773  0.18425818 #> 44  0.4796500 -0.65544436 -0.11063743 #> 45  0.4737102 -0.05879126  0.68993753 #> 46  0.6224641 -0.44103680  0.11062341 #> 47  0.8041649 -0.20072306  0.23829165 #> 48  0.6441458 -0.17385456  0.07400396 #> 49  0.5480420 -0.78238693 -0.54016365 #> 50  0.5480420 -0.78238693 -0.54016365 #> 51  0.7089199 -0.17563653  0.37654151 #> 52  0.3769539 -0.82826593  0.23448758 #> 53  0.6834596 -0.07578277  0.01909532 #> 54  0.2527094 -0.21569803  0.25635943 #> 55  0.6431052 -0.37166373 -0.12400846 #> 56  0.4410245 -0.51368224  0.18837049 #> 57  0.7740971 -0.30891172  0.57370521 #> 58  0.2319779 -0.86032500 -0.48170475 #> 59  0.5716248 -0.36818884  0.36830570 #> 60  0.6280538 -0.23392662  0.44682453 #> 61  0.5756166 -0.54744862 -0.11639086 #> 62  0.5716248 -0.36818884  0.36830570 #> 63  0.6014266 -0.46330347  0.48532615 #> 64  0.4621170 -0.71068616  0.26608879 #> 65  0.2708922 -0.52232311 -0.08780402 #> 66  0.5739246 -0.05601440  0.71549851 #> 67  0.5716248 -0.36818884  0.36830570 #> 68  0.3946202 -0.37801414  0.10956928 #> 69  0.6631807  0.37417320  1.32753128 #> 70  0.6224641 -0.44103680  0.11062341 #> 71  0.6177660  0.34344899  0.79709830 #> 72  0.4796500 -0.65544436 -0.11063743 #> 73  0.6280538 -0.23392662  0.44682453 #> 74  0.2324490 -0.80580463 -0.28471752 #> 75  0.3270809 -0.20504934  0.30219971 #> 76  0.6224641 -0.44103680  0.11062341 #> 77  0.7089199 -0.17563653  0.37654151 #> 78  0.6224641 -0.44103680  0.11062341 #> 79  0.5716248 -0.36818884  0.36830570 #> 80  0.5716248 -0.36818884  0.36830570 #> 81  0.5756166 -0.54744862 -0.11639086 #> 82  0.3769539 -0.82826593  0.23448758 #> 83  0.6947877 -0.46349506  0.19509695 #> 84  0.6504014 -0.46513521  0.43653693 #> 85  0.5756166 -0.54744862 -0.11639086 #> 86  0.6224641 -0.44103680  0.11062341 #> 87  0.2944519 -0.31777262  0.32041506 #> 88  0.5065188 -0.43510689  0.59017798 #> 89  0.6354166 -0.31357126  0.73866202 #> 90  0.5065188 -0.43510689  0.59017798 #> 91  0.3951683 -0.42558280  0.21811956 #> 92  0.2486862 -0.80758491 -0.05621151 #> 93  0.7089199 -0.17563653  0.37654151 #> 94  0.4796500 -0.65544436 -0.11063743 #> 95  0.5065188 -0.43510689  0.59017798 #> 96  0.3946202 -0.37801414  0.10956928 #> 97  0.2196114 -0.43600196  0.46038729 #> 98  0.1867108 -0.72833532  0.32243635 #> 99  0.2496268 -0.29416330  0.40590506 #> 100 0.6014266 -0.46330347  0.48532615 #>  #> $model #> ── Solved rxode2 object ── #> ── Parameters ($params): ── #> # A tibble: 100 × 8 #>    sim.id THETA_Cl THETA_Vc THETA_Ka prop.sd ETA_Cl  ETA_Vc ETA_Ka #>     <int>    <dbl>    <dbl>    <dbl>   <dbl>  <dbl>   <dbl>  <dbl> #>  1      1        4       70        1   0.224  0.650 -0.465   0.437 #>  2      2        4       70        1   0.224  0.800 -0.0334  0.167 #>  3      3        4       70        1   0.224  0.574 -0.0560  0.715 #>  4      4        4       70        1   0.224  0.709 -0.176   0.377 #>  5      5        4       70        1   0.224  0.622 -0.441   0.111 #>  6      6        4       70        1   0.224  0.576 -0.547  -0.116 #>  7      7        4       70        1   0.224  0.210 -0.805  -0.395 #>  8      8        4       70        1   0.224  0.441 -0.514   0.188 #>  9      9        4       70        1   0.224  0.804 -0.201   0.238 #> 10     10        4       70        1   0.224  0.327 -0.205   0.302 #> # ℹ 90 more rows #> ── Initial Conditions ($inits): ── #> depot centr   AUC  #>     0     0     0  #>  #> Simulation without uncertainty in parameters, omega, or sigma matricies #>  #> ── First part of data (object): ── #> # A tibble: 200 × 14 #>   sim.id  time  TVCl  TVVc  TVKa    Cl    Vc    Ka   K20  rxCc    Cc   depot #>    <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>   <dbl> #> 1      1     1     4    70     1  7.67  44.0  1.55 0.174 28.5  28.5  6.42e+2 #> 2      1    14     4    70     1  7.67  44.0  1.55 0.174  4.66  4.66 1.18e-6 #> 3      2     1     4    70     1  8.90  67.7  1.18 0.131 16.2  16.2  8.37e+2 #> 4      2    14     4    70     1  8.90  67.7  1.18 0.131  5.45  5.45 1.79e-4 #> 5      3     1     4    70     1  7.10  66.2  2.05 0.107 22.2  22.2  4.50e+2 #> 6      3    14     4    70     1  7.10  66.2  2.05 0.107  7.30  7.30 1.20e-9 #> # ℹ 194 more rows #> # ℹ 2 more variables: centr <dbl>, AUC <dbl> #>"},{"path":"https://levenc.github.io/posologyr/reference/poso_inter_cmin.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the optimal dosing interval to consistently achieve a target trough concentration (Cmin) — poso_inter_cmin","title":"Estimate the optimal dosing interval to consistently achieve a target trough concentration (Cmin) — poso_inter_cmin","text":"Estimates optimal dosing interval consistently achieve target Cmin, given dose, population pharmacokinetic model, set individual parameters, target concentration.","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_inter_cmin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the optimal dosing interval to consistently achieve a target trough concentration (Cmin) — poso_inter_cmin","text":"","code":"poso_inter_cmin(   dat = NULL,   prior_model = NULL,   dose,   target_cmin,   cmt_dose = 1,   endpoint = \"Cc\",   estim_method = \"map\",   nocb = FALSE,   p = NULL,   greater_than = TRUE,   starting_interval = 12,   add_dose = 10,   duration = 0,   indiv_param = NULL )"},{"path":"https://levenc.github.io/posologyr/reference/poso_inter_cmin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the optimal dosing interval to consistently achieve a target trough concentration (Cmin) — poso_inter_cmin","text":"dat Dataframe. individual subject dataset following structure NONMEM/rxode2 event records. prior_model posologyr prior population pharmacokinetics model, list six objects. dose Numeric. dose given. target_cmin Numeric. Target trough concentration (Cmin). cmt_dose Character numeric. compartment dose administered. Must match one compartments prior model. Defaults 1. endpoint Character. endpoint prior model optimised . default \"Cc\", central concentration. estim_method character string. estimation method used individual parameters. default method \"map\" Maximum Posteriori estimation, method \"prior\" simulates prior population model, \"sir\" uses Sequential Importance Resampling algorithm estimate posteriori distribution individual parameters. argument ignored indiv_param provided. nocb boolean. time-varying covariates: next observation carried backward (nocb) interpolation style, similar NONMEM.  FALSE, last observation carried forward (locf) style used. Defaults FALSE. p Numeric. proportion distribution concentrations consider optimization. Mandatory estim_method=sir. greater_than boolean. TRUE: targets dose leading proportion p concentrations greater target_conc. Respectively, lower FALSE. starting_interval Numeric. Starting inter-dose interval optimization algorithm. add_dose Numeric. Additional doses administered inter-dose interval first dose. duration Numeric. Duration infusion, zero-order administrations. indiv_param Optional. set individual parameters : THETA, estimates ETA, covariates.","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_inter_cmin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the optimal dosing interval to consistently achieve a target trough concentration (Cmin) — poso_inter_cmin","text":"list containing following components: interval Numeric. inter-dose interval reach target trough concentration dosing multiple dose regimen. type_of_estimate Character string. type estimate individual parameters. Either point estimate, distribution. conc_estimate vector numeric estimates conc. Either single value (point estimate ETA), distribution. indiv_param data.frame. set individual parameters used determination optimal dose : THETA, estimates ETA, covariates","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_inter_cmin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate the optimal dosing interval to consistently achieve a target trough concentration (Cmin) — poso_inter_cmin","text":"","code":"rxode2::setRxThreads(2L) # limit the number of threads  # model mod_run001 <- function() {   ini({     THETA_Cl <- 4.0     THETA_Vc <- 70.0     THETA_Ka <- 1.0     ETA_Cl ~ 0.2     ETA_Vc ~ 0.2     ETA_Ka ~ 0.2     prop.sd <- sqrt(0.05)   })   model({     TVCl <- THETA_Cl     TVVc <- THETA_Vc     TVKa <- THETA_Ka      Cl <- TVCl*exp(ETA_Cl)     Vc <- TVVc*exp(ETA_Vc)     Ka <- TVKa*exp(ETA_Ka)      K20 <- Cl/Vc     Cc <- centr/Vc      d/dt(depot) = -Ka*depot     d/dt(centr) = Ka*depot - K20*centr     Cc ~ prop(prop.sd)   }) } # df_patient01: event table for Patient01, following a 30 minutes intravenous # infusion df_patient01 <- data.frame(ID=1,                         TIME=c(0.0,1.0,14.0),                         DV=c(NA,25.0,5.5),                         AMT=c(2000,0,0),                         EVID=c(1,0,0),                         DUR=c(0.5,NA,NA)) # estimate the optimal interval to reach a cmin of of 2.5 mg/l # before each administration poso_inter_cmin(dat=df_patient01,prior_model=mod_run001, dose=1500,duration=0.5,target_cmin=2.5) #>   #>   #>   #>   #> $interval #> [1] 17.76028 #>  #> $type_of_estimate #> [1] \"point estimate\" #>  #> $conc_estimate #> [1] 2.500425 #>  #> $indiv_param #>   THETA_Cl THETA_Vc THETA_Ka   prop.sd    ETA_Cl     ETA_Vc    ETA_Ka #> 1        4       70        1 0.2236068 0.6019035 -0.4291739 0.1278471 #>"},{"path":"https://levenc.github.io/posologyr/reference/poso_simu_pop.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the prior distribution of population parameters — poso_simu_pop","title":"Estimate the prior distribution of population parameters — poso_simu_pop","text":"Estimates prior distribution population parameters Monte Carlo simulations","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_simu_pop.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the prior distribution of population parameters — poso_simu_pop","text":"","code":"poso_simu_pop(   dat = NULL,   prior_model = NULL,   n_simul = 1000,   return_model = TRUE )"},{"path":"https://levenc.github.io/posologyr/reference/poso_simu_pop.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the prior distribution of population parameters — poso_simu_pop","text":"dat Dataframe. individual subject dataset following structure NONMEM/rxode2 event records. prior_model posologyr prior population pharmacokinetics model, list six objects. n_simul integer, number simulations run. n_simul =0, ETAs set 0. return_model boolean. Returns rxode2 model using simulated ETAs set TRUE.","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_simu_pop.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the prior distribution of population parameters — poso_simu_pop","text":"return_model set FALSE, list one element: dataframe $eta individual values ETA. return_model set TRUE, list dataframe individual values ETA, $model rxode2 model using estimated ETAs, $event data.table used solve returned rxode2 model.","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_simu_pop.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate the prior distribution of population parameters — poso_simu_pop","text":"","code":"# model mod_run001 <- function() {   ini({     THETA_Cl <- 4.0     THETA_Vc <- 70.0     THETA_Ka <- 1.0     ETA_Cl ~ 0.2     ETA_Vc ~ 0.2     ETA_Ka ~ 0.2     prop.sd <- sqrt(0.05)   })   model({     TVCl <- THETA_Cl     TVVc <- THETA_Vc     TVKa <- THETA_Ka      Cl <- TVCl*exp(ETA_Cl)     Vc <- TVVc*exp(ETA_Vc)     Ka <- TVKa*exp(ETA_Ka)      K20 <- Cl/Vc     Cc <- centr/Vc      d/dt(depot) = -Ka*depot     d/dt(centr) = Ka*depot - K20*centr     Cc ~ prop(prop.sd)   }) } # df_patient01: event table for Patient01, following a 30 minutes intravenous # infusion df_patient01 <- data.frame(ID=1,                         TIME=c(0.0,1.0,14.0),                         DV=c(NA,25.0,5.5),                         AMT=c(2000,0,0),                         EVID=c(1,0,0),                         DUR=c(0.5,NA,NA)) # estimate the prior distribution of population parameters poso_simu_pop(dat=df_patient01,prior_model=mod_run001,n_simul=100) #>   #>   #>   #>   #> $eta #>          ETA_Cl        ETA_Vc       ETA_Ka #> 1    0.12788489 -0.8474363696  0.972933500 #> 2    0.15044931 -0.6165957725 -0.288257826 #> 3   -0.26290512  0.5912277997 -1.384240405 #> 4    0.06358117 -1.0524378667  0.292384536 #> 5   -0.04738660  0.3020810818  0.017593578 #> 6    0.08829972  1.0646795643  0.177153842 #> 7   -0.56754035 -0.3004684498  0.086744472 #> 8   -0.17353057  0.0297399528 -0.239102222 #> 9   -0.37204606 -0.0681723758  0.667158179 #> 10   0.13349896  0.0284245681  0.181822898 #> 11  -0.12622407  0.6860145096 -0.234266909 #> 12  -0.24704093 -0.0306282674 -0.549257201 #> 13  -0.16917794  0.4382400402 -0.012830985 #> 14   0.19623634  0.7074781593 -0.136171803 #> 15   0.28794308 -0.0661337956 -0.073951692 #> 16  -0.10032863 -0.0136452244 -0.871130218 #> 17  -0.37228285 -0.0663627695 -0.336515849 #> 18   0.50185856 -0.1880645348  0.398147597 #> 19   0.16715147  0.7078051051 -0.294626263 #> 20   0.24462343 -0.0151938765 -0.366482185 #> 21  -0.78361129 -0.4687277771  0.172885315 #> 22  -0.14383402  0.6361751832 -0.223703774 #> 23  -0.14275268 -0.4155070297  0.001112369 #> 24   0.07919262 -0.0923541265 -1.028548245 #> 25   0.32541141 -0.3991711915  0.158204676 #> 26  -0.41127178  0.2498722022  0.534857269 #> 27  -0.24468315  0.1749402395  0.541677063 #> 28  -0.20073218  0.1098069426  0.994336524 #> 29  -0.28347955 -0.4408523470  0.175145338 #> 30   0.06856882  0.0635693077  0.328021625 #> 31   0.72545211 -0.3352657120 -0.722663525 #> 32   0.25653428  0.0234340351 -0.406303331 #> 33   0.37959016  0.3802766057 -0.076283200 #> 34  -0.31170568  0.7406108899 -0.065524473 #> 35   0.05754786  0.7310744062 -0.549295961 #> 36   0.08559765  0.1137295326 -0.326162522 #> 37   0.12743126  0.4451818055 -0.237158833 #> 38  -0.06270716 -0.8739258173  0.548334921 #> 39  -0.55583095 -0.3039715802  0.445685750 #> 40  -0.12034899 -0.2824848002  0.556723874 #> 41   0.41352429 -0.3565260587 -0.343045638 #> 42  -0.25671247  0.4915157307  0.366315463 #> 43  -0.01111643 -0.4709172790 -0.087432011 #> 44  -0.38215972 -0.7138946687 -0.677261388 #> 45  -0.76348667  0.1181884539 -0.546778736 #> 46  -0.21289274  0.2845195196  0.837286700 #> 47   0.12095204  0.1063290947  1.054290892 #> 48  -0.65677056  0.6579158907  0.208758285 #> 49  -0.01757885 -0.0588808083  0.371926925 #> 50  -0.24673067 -0.1805240163 -0.097830097 #> 51   0.39191046  0.4159538681 -0.037967541 #> 52  -0.87001416 -0.0101960308 -0.244342202 #> 53   0.69353025  0.3160638259  0.007746790 #> 54   0.76718629 -0.5305894330  0.687736930 #> 55  -0.15600010 -0.5372649408  0.233664676 #> 56  -0.22696478  0.3977303316 -0.325538825 #> 57   0.34939402  0.3356843830 -0.460650352 #> 58  -0.33446585 -0.4592726994 -0.762361942 #> 59   0.56771730 -0.0018453990  0.164049535 #> 60   0.02221040 -0.2074472251 -0.245654268 #> 61  -0.43756583  0.4004970612 -0.323059623 #> 62   0.22864983 -0.1643911881 -0.385340511 #> 63   0.04960210 -0.9753202418 -0.156391704 #> 64   0.41269427  0.9290324438  0.488513823 #> 65   0.48279891  0.5271998013 -0.145369898 #> 66  -0.01509115 -0.3794736224  0.633903308 #> 67   0.07928208  0.2879443073 -0.147487550 #> 68   0.20664080  0.0326334612  0.362415054 #> 69   0.06363990  0.8187491113  0.398934107 #> 70  -0.06412535  0.4812014182 -0.191628561 #> 71   0.44473418  0.3355002381 -0.190077991 #> 72  -0.35786214 -0.6259152421  0.309481419 #> 73   0.39099910 -0.8330321749  0.258486502 #> 74   0.60591300  0.7522020684  0.003734992 #> 75   0.14105591 -0.0772794560  0.199033893 #> 76   0.11255801  0.0686682135 -0.014146813 #> 77   0.29403252 -0.7716550381 -0.377551574 #> 78  -0.10415015 -0.4098232879 -0.277269197 #> 79   0.04687274  0.3381934530  0.466881915 #> 80  -0.01657469 -0.0004508222 -0.274059996 #> 81   0.34703845 -0.1353410256 -0.518273028 #> 82  -0.77759568  0.0783792867 -0.009475904 #> 83   0.09837731 -0.2966746333 -0.151063236 #> 84   0.86918648  0.5425733777 -0.484762337 #> 85  -0.20056939 -0.0337251850  0.880821557 #> 86   0.22423405  0.2266253611  0.098053962 #> 87  -0.06289958 -0.0805403600 -0.328896523 #> 88   0.91974567  1.2345015201  1.176161346 #> 89   0.87918576  0.2155617721  0.643564140 #> 90  -0.82927912 -0.3592766936 -0.700110707 #> 91  -0.34914053  0.0990080614 -0.229746377 #> 92  -0.22935254 -0.0841394013 -0.248344505 #> 93  -0.22056017  0.1123495210  0.058659532 #> 94  -0.24618351  0.1502172258 -0.043350296 #> 95   0.61466996  0.3262538319 -0.020061632 #> 96  -0.26323832 -0.1269916789  0.930418768 #> 97   0.73455148 -0.0915474373  0.805957437 #> 98  -0.03921193  0.1359026367  0.274076390 #> 99   0.14639569 -0.1606572049 -0.790413751 #> 100  0.08872456  0.5419509531 -0.251199157 #>  #> $model #> ── Solved rxode2 object ── #> ── Parameters ($params): ── #> # A tibble: 100 × 8 #>    sim.id THETA_Cl THETA_Vc THETA_Ka prop.sd  ETA_Cl  ETA_Vc  ETA_Ka #>     <int>    <dbl>    <dbl>    <dbl>   <dbl>   <dbl>   <dbl>   <dbl> #>  1      1        4       70        1   0.224  0.128  -0.847   0.973  #>  2      2        4       70        1   0.224  0.150  -0.617  -0.288  #>  3      3        4       70        1   0.224 -0.263   0.591  -1.38   #>  4      4        4       70        1   0.224  0.0636 -1.05    0.292  #>  5      5        4       70        1   0.224 -0.0474  0.302   0.0176 #>  6      6        4       70        1   0.224  0.0883  1.06    0.177  #>  7      7        4       70        1   0.224 -0.568  -0.300   0.0867 #>  8      8        4       70        1   0.224 -0.174   0.0297 -0.239  #>  9      9        4       70        1   0.224 -0.372  -0.0682  0.667  #> 10     10        4       70        1   0.224  0.133   0.0284  0.182  #> # ℹ 90 more rows #> ── Initial Conditions ($inits): ── #> depot centr   AUC  #>     0     0     0  #>  #> Simulation without uncertainty in parameters, omega, or sigma matricies #>  #> ── First part of data (object): ── #> # A tibble: 15,100 × 14 #>   sim.id  time  TVCl  TVVc  TVKa    Cl    Vc    Ka   K20  rxCc    Cc depot centr #>    <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> #> 1      1   0       4    70     1  4.55  30.0  2.65 0.152  0     0       0    0   #> 2      1   0.1     4    70     1  4.55  30.0  2.65 0.152  1.61  1.61  351.  48.3 #> 3      1   0.2     4    70     1  4.55  30.0  2.65 0.152  5.90  5.90  621. 177.  #> 4      1   0.3     4    70     1  4.55  30.0  2.65 0.152 12.2  12.2   828. 366.  #> 5      1   0.4     4    70     1  4.55  30.0  2.65 0.152 20.0  20.0   987. 600.  #> 6      1   0.5     4    70     1  4.55  30.0  2.65 0.152 28.9  28.9  1109. 867.  #> # ℹ 15,094 more rows #> # ℹ 1 more variable: AUC <dbl> #>  #> $event #> ── EventTable with 152 records ── #> 1 dosing records (see $get.dosing(); add with add.dosing or et) #> 151 observation times (see $get.sampling(); add with add.sampling or et) #> ── First part of : ── #> # A tibble: 152 × 5 #>       id  time   amt evid          dur        #>    <int> <dbl> <dbl> <evid>        <rate/dur> #>  1     1   0      NA 0:Observation NA         #>  2     1   0    2000 1:Dose (Add)   0.5       #>  3     1   0.1    NA 0:Observation NA         #>  4     1   0.2    NA 0:Observation NA         #>  5     1   0.3    NA 0:Observation NA         #>  6     1   0.4    NA 0:Observation NA         #>  7     1   0.5    NA 0:Observation NA         #>  8     1   0.6    NA 0:Observation NA         #>  9     1   0.7    NA 0:Observation NA         #> 10     1   0.8    NA 0:Observation NA         #> # ℹ 142 more rows #>"},{"path":"https://levenc.github.io/posologyr/reference/poso_time_cmin.html","id":null,"dir":"Reference","previous_headings":"","what":"Estimate the time required to reach a target trough concentration (Cmin) — poso_time_cmin","title":"Estimate the time required to reach a target trough concentration (Cmin) — poso_time_cmin","text":"Estimates time required reach target trough concentration (Cmin) given population pharmacokinetic model, set individual parameters, dose, target Cmin.","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_time_cmin.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Estimate the time required to reach a target trough concentration (Cmin) — poso_time_cmin","text":"","code":"poso_time_cmin(   dat = NULL,   prior_model = NULL,   tdm = FALSE,   target_cmin,   dose = NULL,   cmt_dose = 1,   endpoint = \"Cc\",   estim_method = \"map\",   nocb = FALSE,   p = NULL,   greater_than = TRUE,   from = 0.2,   last_time = 72,   add_dose = NULL,   interdose_interval = NULL,   duration = 0,   indiv_param = NULL )"},{"path":"https://levenc.github.io/posologyr/reference/poso_time_cmin.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Estimate the time required to reach a target trough concentration (Cmin) — poso_time_cmin","text":"dat Dataframe. individual subject dataset following structure NONMEM/rxode2 event records. prior_model posologyr prior population pharmacokinetics model, list six objects. tdm boolean. TRUE: computes predicted time reach target trough concentration (Cmin) following last event dat, using Maximum Posteriori estimation. Setting tdm TRUE causes following occur: simulation  starts time last recorded dose (TDM data) plus ; simulation stops time last recorded dose (TDM data) plus last_time; arguments dose, duration, estim_method, p, greater_than, interdose_interval, add_dose, indiv_param starting_time ignored. target_cmin Numeric. Target trough concentration (Cmin). dose Numeric. Dose administered. argument ignored tdm set TRUE. cmt_dose Character numeric. compartment dose administered. Must match one compartments prior model. Defaults 1. endpoint Character. endpoint prior model optimised . default \"Cc\", central concentration. estim_method character string. estimation method used individual parameters. default method \"map\" Maximum Posteriori estimation, method \"prior\" simulates prior population model, \"sir\" uses Sequential Importance Resampling algorithm estimate posteriori distribution individual parameters. argument ignored indiv_param provided, tdm set TRUE. nocb boolean. time-varying covariates: next observation carried backward (nocb) interpolation style, similar NONMEM. FALSE, last observation carried forward (locf) style used. Defaults FALSE. p Numeric. proportion distribution Cmin consider estimation. Mandatory estim_method=sir. argument ignored tdm set TRUE. greater_than boolean. TRUE: targets time leading proportion p cmins greater target_cmin. Respectively, lower FALSE. argument ignored tdm set TRUE. Numeric. Starting time simulation individual time-concentration profile. default value 0.2. tdm set TRUE simulation starts time last recorded dose plus . last_time Numeric. Ending time simulation individual time-concentration profile. default value 72. tdm set TRUE simulation stops time last recorded dose plus last_time. add_dose Numeric. Additional doses administered inter-dose interval first dose. Optional. argument ignored tdm set TRUE. interdose_interval Numeric. Time inter-dose interval multiple dose regimen. Must provided add_dose used. argument ignored tdm set TRUE. duration Numeric. Duration infusion, zero-order administrations. argument ignored tdm set TRUE. indiv_param Optional. set individual parameters : THETA, estimates ETA, covariates.","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_time_cmin.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Estimate the time required to reach a target trough concentration (Cmin) — poso_time_cmin","text":"list containing following components: time Numeric. Time needed reach selected Cmin. type_of_estimate Character string. type estimate individual parameters. Either point estimate, distribution. cmin_estimate vector numeric estimates Cmin. Either single value (point estimate ETA), distribution. indiv_param data.frame. set individual parameters used determination time needed reach selected Cmin: THETA, estimates ETA, covariates","code":""},{"path":"https://levenc.github.io/posologyr/reference/poso_time_cmin.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Estimate the time required to reach a target trough concentration (Cmin) — poso_time_cmin","text":"","code":"rxode2::setRxThreads(2L) # limit the number of threads  # model mod_run001 <- function() {   ini({     THETA_Cl <- 4.0     THETA_Vc <- 70.0     THETA_Ka <- 1.0     ETA_Cl ~ 0.2     ETA_Vc ~ 0.2     ETA_Ka ~ 0.2     prop.sd <- sqrt(0.05)   })   model({     TVCl <- THETA_Cl     TVVc <- THETA_Vc     TVKa <- THETA_Ka      Cl <- TVCl*exp(ETA_Cl)     Vc <- TVVc*exp(ETA_Vc)     Ka <- TVKa*exp(ETA_Ka)      K20 <- Cl/Vc     Cc <- centr/Vc      d/dt(depot) = -Ka*depot     d/dt(centr) = Ka*depot - K20*centr     Cc ~ prop(prop.sd)   }) } # df_patient01: event table for Patient01, following a 30 minutes intravenous # infusion df_patient01 <- data.frame(ID=1,                         TIME=c(0.0,1.0,14.0),                         DV=c(NA,25.0,5.5),                         AMT=c(2000,0,0),                         EVID=c(1,0,0),                         DUR=c(0.5,NA,NA)) # predict the time needed to reach a concentration of 2.5 mg/l # after the administration of a 2500 mg dose over a 30 minutes # infusion poso_time_cmin(dat=df_patient01,prior_model=mod_run001, dose=2500,duration=0.5,from=0.5,target_cmin=2.5) #>   #>   #>   #>   #> $time #> [1] 20.5 #>  #> $type_of_estimate #> [1] \"point estimate\" #>  #> $cmin_estimate #> [1] 2.489933 #>  #> $indiv_param #>   THETA_Cl THETA_Vc THETA_Ka   prop.sd    ETA_Cl     ETA_Vc   ETA_Ka #> 1        4       70        1 0.2236068 0.6019037 -0.4291733 0.127848 #>"},{"path":"https://levenc.github.io/posologyr/reference/posologyr_error_lines.html","id":null,"dir":"Reference","previous_headings":"","what":"This creates the posologyr error lines from a rxui model — posologyr_error_lines","title":"This creates the posologyr error lines from a rxui model — posologyr_error_lines","text":"creates posologyr error lines rxui model","code":""},{"path":"https://levenc.github.io/posologyr/reference/posologyr_error_lines.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"This creates the posologyr error lines from a rxui model — posologyr_error_lines","text":"","code":"posologyr_error_lines(line)  # S3 method for class 'norm' posologyr_error_lines(line)  # S3 method for class 't' posologyr_error_lines(line)  # Default S3 method posologyr_error_lines(line)  # S3 method for class 'rxUi' posologyr_error_lines(line)"},{"path":"https://levenc.github.io/posologyr/reference/posologyr_error_lines.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"This creates the posologyr error lines from a rxui model — posologyr_error_lines","text":"line line parse","code":""},{"path":"https://levenc.github.io/posologyr/reference/posologyr_error_lines.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"This creates the posologyr error lines from a rxui model — posologyr_error_lines","text":"error lines posology","code":""},{"path":"https://levenc.github.io/posologyr/reference/posologyr_error_lines.html","id":"author","dir":"Reference","previous_headings":"","what":"Author","title":"This creates the posologyr error lines from a rxui model — posologyr_error_lines","text":"Matthew L. Fidler","code":""},{"path":[]},{"path":"https://levenc.github.io/posologyr/news/index.html","id":"additional-feature-1-2-5","dir":"Changelog","previous_headings":"","what":"Additional feature","title":"posologyr v1.2.5","text":"route administration (.e. compartment drug administered) can now specified poso_time_cmin(), poso_dose_conc(), poso_dose_auc() poso_inter_cmin(). poso_simu_pop() provides rxode2 model using simulated ETA input dataset, interpolation covariates, make plotting easier","code":""},{"path":"https://levenc.github.io/posologyr/news/index.html","id":"documentation-1-2-5","dir":"Changelog","previous_headings":"","what":"Documentation","title":"posologyr v1.2.5","text":"README illustrates simple example dose adaptation vignette(\"route_of_administration\") shows select route administration optimal dosing vignette(\"population_models\") describes structure prior population models written model functions can parsed rxode2 used posologyr vignette(\"posologyr_user_defined_models\") renamed vignette(\"classic_posologyr_models\") Examples use rxode2 model functions","code":""},{"path":"https://levenc.github.io/posologyr/news/index.html","id":"bug-fix-1-2-5","dir":"Changelog","previous_headings":"","what":"Bug fix","title":"posologyr v1.2.5","text":"Fix bug poso_estim_map(), poso_estim_sir() poso_simu_pop() failed models featuring single parameter IIV.","code":""},{"path":"https://levenc.github.io/posologyr/news/index.html","id":"posologyr-v124","dir":"Changelog","previous_headings":"","what":"posologyr v1.2.4","title":"posologyr v1.2.4","text":"CRAN release: 2024-02-09 Add ability use rxode2 ui models poso_* functions. model parsed rxode2() package model$posologyr gives list needed poso_* functions","code":""},{"path":[]},{"path":"https://levenc.github.io/posologyr/news/index.html","id":"bug-fix-1-2-3","dir":"Changelog","previous_headings":"","what":"Bug fix","title":"posologyr v1.2.3","text":"Fix bug poso_dose_conc(), poso_dose_auc() poso_inter_cmin() returned estimate target value optimized always equal zero.","code":""},{"path":"https://levenc.github.io/posologyr/news/index.html","id":"documentation-1-2-3","dir":"Changelog","previous_headings":"","what":"Documentation","title":"posologyr v1.2.3","text":"documentation poso_time_cmin(), poso_dose_conc(), poso_dose_auc() now explicitly states consequences setting tdm TRUE: parameters required, parameters ignored, parameters behave differently. functions poso_time_cmin(), poso_dose_conc(), poso_dose_auc() now return warning input parameters ignored. Fix incorrect information regarding duration AUC documentation poso_dose_auc()","code":""},{"path":"https://levenc.github.io/posologyr/news/index.html","id":"posologyr-v122","dir":"Changelog","previous_headings":"","what":"posologyr v1.2.2","title":"posologyr v1.2.2","text":"CRAN release: 2023-06-12 Relax requirements NONMEM comparison test time-varying covariates account computational differences observed alternative BLAS ATLAS CRAN.","code":""},{"path":"https://levenc.github.io/posologyr/news/index.html","id":"posologyr-v121","dir":"Changelog","previous_headings":"","what":"posologyr v1.2.1","title":"posologyr v1.2.1","text":"CRAN release: 2023-06-05 Add reference Kang et al. (2012) doi:10.4196/kjpp.2012.16.2.97 DESCRIPTION (requested CRAN) Fix messages console internal function posologyr() (requested CRAN) Fix assignment parent environment dose optim functions, using parent.frame() (requested CRAN)","code":""},{"path":[]},{"path":"https://levenc.github.io/posologyr/news/index.html","id":"additional-features-1-2-0","dir":"Changelog","previous_headings":"","what":"Additional features","title":"posologyr v1.2.0","text":"poso_estim_map(), poso_estim_sir() poso_estim_mcmc() can now estimate individual PK profiles multiple endpoints models (eg. PK-PD, parent-metabolite, blood-CSF…), using different residual error model endpoint. poso_time_cmin(), poso_dose_conc(), poso_dose_auc() poso_inter_cmin() now allow select end point interest want optimise, provided defined model.","code":""},{"path":"https://levenc.github.io/posologyr/news/index.html","id":"documentation-1-2-0","dir":"Changelog","previous_headings":"","what":"Documentation","title":"posologyr v1.2.0","text":"vignette(\"a_priori_dosing\") illustrates priori dose selection vignette(\"a_posteriori_dosing\") illustrates posteriori dose selection, using TDM data vignette(\"auc_based_dosing\") shows select optimal dose given target AUC using data TDM vignette(\"multiple_endpoints\") introduces new multiple endpoints feature","code":""},{"path":"https://levenc.github.io/posologyr/news/index.html","id":"internal-changes-1-2-0","dir":"Changelog","previous_headings":"","what":"Internal changes","title":"posologyr v1.2.0","text":"description package updated","code":""},{"path":[]},{"path":"https://levenc.github.io/posologyr/news/index.html","id":"additional-features-1-1-0","dir":"Changelog","previous_headings":"","what":"Additional features","title":"posologyr v1.1.0","text":"poso_time_cmin() can now estimate time needed reach selected trough concentration (Cmin) using data TDM directly poso_dose_conc() can now estimate optimal dose reach target concentration following events TDM poso_dose_auc() can now estimate optimal dose reach target auc following events TDM","code":""},{"path":[]},{"path":"https://levenc.github.io/posologyr/news/index.html","id":"breaking-changes-1-0-0","dir":"Changelog","previous_headings":"","what":"Breaking changes","title":"posologyr v1.0.0","text":"posologyr() now internal function, exported functions take patient data prior model input parameters adaptive MAP forecasting option removed","code":""},{"path":"https://levenc.github.io/posologyr/news/index.html","id":"additional-features-1-0-0","dir":"Changelog","previous_headings":"","what":"Additional features","title":"posologyr v1.0.0","text":"poso_estim_map() provides rxode2 model using MAP-EBE input dataset, interpolation covariates, make plotting easier","code":""},{"path":"https://levenc.github.io/posologyr/news/index.html","id":"internal-changes-1-0-0","dir":"Changelog","previous_headings":"","what":"Internal changes","title":"posologyr v1.0.0","text":"RxODE import updated rxode2 tests updated take account internalization posologyr() function","code":""},{"path":"https://levenc.github.io/posologyr/news/index.html","id":"bug-fixes-1-0-0","dir":"Changelog","previous_headings":"","what":"Bug fixes","title":"posologyr v1.0.0","text":"poso_time_cmin(), poso_dose_auc(), poso_dose_conc(), poso_inter_cmin() longer fail models IOV","code":""},{"path":"https://levenc.github.io/posologyr/news/index.html","id":"posologyr-v020","dir":"Changelog","previous_headings":"","what":"posologyr v0.2.0","title":"posologyr v0.2.0","text":"poso_estim_sir() estimates posterior distribution individual parameters Sequential Importance Resampling (SIR). roughly 25 times faster poso_estim_mcmc() 1000 samples. poso_estim_map() allows estimation individual parameters adaptive MAP forecasting (cf. doi: 10.1007/s11095-020-02908-7) adapt=TRUE. poso_simu_pop(), poso_estim_map(), poso_estim_sir() now support models inter-individual (IIV) inter-occasion variability (IOV). MASS:mvrnorm replaced mvtnorm::rmvnorm multivariate normal distributions. Input validation added exported functions. poso_estim_map() now uses method=“L-BFGS-B” optim better convergence algorithm. poso_inter_cmin() now uses method=“L-BFGS-B” optim better convergence algorithm. poso_dose_conc() new name poso_dose_ctime(). Issues #5 #6 fixed: poso_time_cmin(), poso_dose_auc(), poso_dose_conc(), poso_inter_cmin() now work prior posterior distributions ETA, point estimates (MAP). new nocb parameter added posologyr(). interpolation method time-varying covariates can either last observation carried forward (locf, RxODE default), next observation carried backward (nocb, NONMEM default). vignette(\"uncertainty_estimates\") removed. built-models removed.","code":""},{"path":"https://levenc.github.io/posologyr/news/index.html","id":"posologyr-v011","dir":"Changelog","previous_headings":"","what":"posologyr v0.1.1","title":"posologyr v0.1.1","text":"poso_time_cmin(), poso_dose_ctime(), poso_dose_auc() now work multiple dose regimen. poso_inter_cmin() allows optimization inter-dose interval multiple dose regimen. vignette(\"case_study_vancomycin\") illustrates AUC-based optimal dosing, multiple dose regimen, continuous intravenous infusion.","code":""},{"path":"https://levenc.github.io/posologyr/news/index.html","id":"posologyr-v010","dir":"Changelog","previous_headings":"","what":"posologyr v0.1.0","title":"posologyr v0.1.0","text":"First public release.","code":""}]