From 4cd86886cd13320b7029fcd55cd17ce8cecc7f54 Mon Sep 17 00:00:00 2001 From: avallecam Date: Thu, 22 Oct 2020 13:53:16 -0500 Subject: [PATCH] upload new webpage with clean readme + intro tab --- docs/404.html | 3 + docs/CODE_OF_CONDUCT.html | 3 + docs/CONTRIBUTING.html | 3 + docs/ISSUE_TEMPLATE.html | 3 + docs/LICENSE.html | 3 + docs/SUPPORT.html | 3 + docs/articles/howto-reprex.html | 30 +- .../figure-html/unnamed-chunk-12-1.png | Bin 24179 -> 24141 bytes docs/articles/index.html | 4 + docs/articles/intro.html | 357 ++++++++++++++++ .../figure-html/unnamed-chunk-10-1.png | Bin 0 -> 20502 bytes .../figure-html/unnamed-chunk-14-1.png | Bin 0 -> 26049 bytes docs/authors.html | 3 + docs/index.html | 381 ++---------------- docs/pkgdown.yml | 1 + docs/reference/correct_sero_misclass.html | 3 + .../figures/README-unnamed-chunk-10-1.png | Bin 7704 -> 7554 bytes .../figures/README-unnamed-chunk-13-1.png | Bin 8195 -> 8500 bytes .../figures/README-unnamed-chunk-14-1.png | Bin 8327 -> 8327 bytes .../figures/README-unnamed-chunk-9-1.png | Bin 0 -> 7516 bytes docs/reference/ggplot_prevalence.html | 42 +- docs/reference/index.html | 5 +- docs/reference/rogan_gladen_stderr_unk.html | 3 + .../sample_posterior_r_mcmc_hyperR.html | 3 + .../sample_posterior_r_mcmc_testun.html | 3 + .../serosvy_known_sample_posterior.html | 3 + .../serosvy_unknown_sample_posterior.html | 3 + docs/reference/srvyr_prop_step_01.html | 3 + docs/reference/unite_dotwhiskers.html | 3 + 29 files changed, 487 insertions(+), 378 deletions(-) create mode 100644 docs/articles/intro.html create mode 100644 docs/articles/intro_files/figure-html/unnamed-chunk-10-1.png create mode 100644 docs/articles/intro_files/figure-html/unnamed-chunk-14-1.png create mode 100644 docs/reference/figures/README-unnamed-chunk-9-1.png diff --git a/docs/404.html b/docs/404.html index 44c47c4..8b41041 100644 --- a/docs/404.html +++ b/docs/404.html @@ -88,6 +88,9 @@
  • Workflow: How to use the serosurvey R package?
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  • + Introduction: serosurvey R package +
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  • Workflow: How to use the serosurvey R package?
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  • Workflow: How to use the serosurvey R package?
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  • Workflow: How to use the serosurvey R package?
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  • Workflow: How to use the serosurvey R package?
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  • Workflow: How to use the serosurvey R package?
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    } toc() -#> 248.79 sec elapsed +#> 573.72 sec elapsed outcome_01_adj <- out %>% mutate(rowname=as.numeric(rowname)) %>% @@ -559,7 +562,7 @@

    83 75.9 (65.3 - 84.6) 78.9 ( 67.5 - 87) -83.132 (65.85 - 95.6) +83.150 (65.89 - 95.5) 0.06 @@ -571,7 +574,7 @@

    20 25.0 ( 8.7 - 49.1) 14.8 ( 3.8 - 44) -0.414 ( 0.28 - 29.9) +0.425 ( 0.28 - 29.9) 0.59 @@ -583,7 +586,7 @@

    23 65.2 (42.7 - 83.6) 44.8 ( 16.0 - 78) -31.518 ( 0.40 - 81.1) +31.506 ( 0.40 - 81.1) 0.38 @@ -693,19 +696,22 @@

    + # theme(axis.text.x = element_text(angle = 0, vjust = 0, hjust=0)) + + scale_y_continuous( + labels = scales::percent_format(accuracy = 1), + breaks = scales::pretty_breaks(n = 5)) + + # coord_flip() + + facet_wrap(denominator~.,scales = "free") + + # facet_grid(denominator~.,scales = "free_y") + + colorspace::scale_color_discrete_qualitative() + + labs(title = "Prevalence of numerators across denominators", + y = "Prevalence",x = "")

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  • Workflow: How to use the serosurvey R package?
    • +
  • + Introduction: serosurvey R package +
    • @@ -121,6 +124,7 @@

    All vignettes

    diff --git a/docs/articles/intro.html b/docs/articles/intro.html new file mode 100644 index 0000000..9ea4134 --- /dev/null +++ b/docs/articles/intro.html @@ -0,0 +1,357 @@ + + + + + + + +Introduction: serosurvey R package • serosurvey + + + + + + + + + + +
    +
    + + + + +
    +
    +
    +

    Introduction: serosurvey R package

    + + + + + +
    + + + +
    +

    +Introduction

    +

    Here we present three examples, definitions and related references:

    +
    library(serosurvey)
    + +
    +

    +2. survey: Estimate multiple prevalences

    +
      +
    • +

      In the Article tab we provide a workflow to estimate multiple prevalences:

      +
        +
      • using different set of covariates and outcomes as numerators or denominators,
        • +
      • in one single pipe operation
        • +
      +
      • +
    +
    # crear matriz
+  #
+  # set 01 of denominator-numerator
+  #
+expand_grid(
+  design=list(design),
+  denominator=c("covariate_01","covariate_02"), # covariates
+  numerator=c("outcome_one","outcome_two") # outcomes
+  ) %>% 
+  #
+  # set 02 of denominator-numerator (e.g. within main outcome)
+  #
+  union_all(
+    expand_grid(
+      design=list(design),
+      denominator=c("outcome_one","outcome_two"), # outcomes
+      numerator=c("covariate_02") # covariates
+    )
+  ) %>% 
+  #
+  # create symbols (to be readed as arguments)
+  #
+  mutate(
+    denominator=map(denominator,dplyr::sym),
+    numerator=map(numerator,dplyr::sym)
+  ) %>% 
+  #
+  # estimate prevalence
+  #
+  mutate(output=pmap(.l = select(.,design,denominator,numerator),
+                     .f = serosvy_proportion)) %>% 
+  #
+  # show the outcome
+  #
+  select(-design,-denominator,-numerator) %>% 
+  unnest(cols = c(output)) %>% 
+  print(n=Inf)
+#> # A tibble: 25 x 23
+#>    denominator denominator_lev~ numerator numerator_level   prop prop_low
+#>    <chr>       <fct>            <chr>     <fct>            <dbl>    <dbl>
+#>  1 covariate_~ E                outcome_~ No              0.211   0.130  
+#>  2 covariate_~ E                outcome_~ Yes             0.789   0.675  
+#>  3 covariate_~ H                outcome_~ No              0.852   0.564  
+#>  4 covariate_~ H                outcome_~ Yes             0.148   0.0377 
+#>  5 covariate_~ M                outcome_~ No              0.552   0.224  
+#>  6 covariate_~ M                outcome_~ Yes             0.448   0.160  
+#>  7 covariate_~ E                outcome_~ (-0.1,50]       0.182   0.0499 
+#>  8 covariate_~ E                outcome_~ (50,100]        0.818   0.515  
+#>  9 covariate_~ H                outcome_~ (-0.1,50]       0.0769  0.00876
+#> 10 covariate_~ H                outcome_~ (50,100]        0.923   0.560  
+#> 11 covariate_~ M                outcome_~ (50,100]        1.00    1.00   
+#> 12 covariate_~ No               outcome_~ No              1.00    1.00   
+#> 13 covariate_~ Yes              outcome_~ No              0.0334  0.00884
+#> 14 covariate_~ Yes              outcome_~ Yes             0.967   0.882  
+#> 15 covariate_~ No               outcome_~ (-0.1,50]       0.218   0.0670 
+#> 16 covariate_~ No               outcome_~ (50,100]        0.782   0.479  
+#> 17 covariate_~ Yes              outcome_~ (-0.1,50]       0.0914  0.0214 
+#> 18 covariate_~ Yes              outcome_~ (50,100]        0.909   0.684  
+#> 19 outcome_one No               covariat~ No              0.939   0.778  
+#> 20 outcome_one No               covariat~ Yes             0.0615  0.0148 
+#> 21 outcome_one Yes              covariat~ Yes             1.00    1.00   
+#> 22 outcome_two (-0.1,50]        covariat~ No              0.549   0.294  
+#> 23 outcome_two (-0.1,50]        covariat~ Yes             0.451   0.219  
+#> 24 outcome_two (50,100]         covariat~ No              0.305   0.188  
+#> 25 outcome_two (50,100]         covariat~ Yes             0.695   0.546  
+#> # ... with 17 more variables: prop_upp <dbl>, prop_cv <dbl>,
+#> #   prop_se <dbl>, total <dbl>, total_low <dbl>, total_upp <dbl>,
+#> #   total_cv <dbl>, total_se <dbl>, total_deff <dbl>, total_den <dbl>,
+#> #   total_den_low <dbl>, total_den_upp <dbl>, raw_num <int>,
+#> #   raw_den <int>, raw_prop <dbl>, raw_prop_low <dbl>, raw_prop_upp <dbl>
    +
    +

    +learnr tutorial

    +
      +
    • Learn to build this with in a tutorial in Spanish:
      • +
    +
    # install learner and run tutorial
+if(!require("learnr")) install.packages("learnr")
+learnr::run_tutorial(name = "taller",package = "serosurvey")
    +
    +
    +
    +

    +3. serology: Estimate prevalence Under misclassification

    +
      +

    • We gather one frequentist approach (Rogan and Gladen 1978), available in different Github repos, that deal with misclassification due to an imperfect diagnostic test (Azman et al. 2020; Takahashi, Greenhouse, and Rodríguez-Barraquer 2020). Check the Reference tab.

      • +

    • We provide tidy outputs for bayesian approaches developed in Daniel B. Larremore et al. (2020) here and Daniel B Larremore et al. (2020) here:

      • +

    • You can use them with purrr and furrr to efficiently iterate and parallelize this step for multiple prevalences. Check the workflow in Article tab.

      • +
    +
    +

    +Known test performance - Bayesian method +

    + +

    +
    example("serosvy_known_sample_posterior")
    +
    +
    +

    +Unknown test performance - Bayesian method +

    +
      +
    • The test performance is called “unknown” or “uncertain” when test sensitivity and specificity are not known with certainty (Kritsotakis 2020; Diggle 2011; Gelman and Carpenter 2020) and lab validation data is available with a limited set of samples, tipically during a novel pathogen outbreak.
      • +
    + +

    +
    example("serosvy_unknown_sample_posterior")
    +
    +
    +
    +
    +

    +Contributing

    +

    Feel free to fill an issue or contribute with your functions or workflows in a pull request.

    +

    Here are a list of publications with interesting approaches using R:

    +
      +

    • Silveira et al. (2020) and Hallal et al. (2020) analysed a serological survey accounting for sampling design and test validity using parametric bootstraping, following Lewis and Torgerson (2012).

      • +

    • Flor et al. (2020) implemented a lot of frequentist and bayesian methods for test with known sensitivity and specificity. Code is available here.

      • +

    • Gelman and Carpenter (2020) also applied Bayesian inference with hierarchical regression and post-stratification to account for test uncertainty with unknown specificity and sensitivity. Here a case-study.

      • +
    +
    +
    +

    +References

    +
    +
    +

    Azman, Andrew S, Stephen Lauer, M. Taufiqur Rahman Bhuiyan, Francisco J Luquero, Daniel T Leung, Sonia Hegde, Jason B Harris, et al. 2020. “Vibrio Cholerae O1 Transmission in Bangladesh: Insights from a Nationally- Representative Serosurvey,” March. https://doi.org/10.1101/2020.03.13.20035352.

    +
    +
    +

    Diggle, Peter J. 2011. “Estimating Prevalence Using an Imperfect Test.” Epidemiology Research International 2011: 1–5. https://doi.org/10.1155/2011/608719.

    +
    +
    +

    Flor, Matthias, Michael Weiß, Thomas Selhorst, Christine Müller-Graf, and Matthias Greiner. 2020. “Comparison of Bayesian and Frequentist Methods for Prevalence Estimation Under Misclassification.” BMC Public Health 20 (1). https://doi.org/10.1186/s12889-020-09177-4.

    +
    +
    +

    Gelman, Andrew, and Bob Carpenter. 2020. “Bayesian Analysis of Tests with Unknown Specificity and Sensitivity.” Journal of the Royal Statistical Society: Series C (Applied Statistics), August. https://doi.org/10.1111/rssc.12435.

    +
    +
    +

    Hallal, Pedro C, Fernando P Hartwig, Bernardo L Horta, Mariângela F Silveira, Claudio J Struchiner, Luı́s P Vidaletti, Nelson A Neumann, et al. 2020. “SARS-CoV-2 Antibody Prevalence in Brazil: Results from Two Successive Nationwide Serological Household Surveys.” The Lancet Global Health, September. https://doi.org/10.1016/s2214-109x(20)30387-9.

    +
    +
    +

    Kritsotakis, Evangelos I. 2020. “On the Importance of Population-Based Serological Surveys of SARS-CoV-2 Without Overlooking Their Inherent Uncertainties.” Public Health in Practice 1 (November): 100013. https://doi.org/10.1016/j.puhip.2020.100013.

    +
    +
    +

    Larremore, Daniel B, Bailey K Fosdick, Kate M Bubar, Sam Zhang, Stephen M Kissler, C. Jessica E. Metcalf, Caroline Buckee, and Yonatan Grad. 2020. “Estimating SARS-CoV-2 Seroprevalence and Epidemiological Parameters with Uncertainty from Serological Surveys.” medRxiv, April. https://doi.org/10.1101/2020.04.15.20067066.

    +
    +
    +

    Larremore, Daniel B., Bailey K. Fosdick, Sam Zhang, and Yonatan H. Grad. 2020. “Jointly Modeling Prevalence, Sensitivity and Specificity for Optimal Sample Allocation.” bioRxiv, May. https://doi.org/10.1101/2020.05.23.112649.

    +
    +
    +

    Lewis, Fraser I, and Paul R Torgerson. 2012. “A Tutorial in Estimating the Prevalence of Disease in Humans and Animals in the Absence of a Gold Standard Diagnostic.” Emerging Themes in Epidemiology 9 (1). https://doi.org/10.1186/1742-7622-9-9.

    +
    +
    +

    Rogan, Walter J., and Beth Gladen. 1978. “Estimating Prevalence from the Results of A Screening Test.” American Journal of Epidemiology 107 (1): 71–76. https://doi.org/10.1093/oxfordjournals.aje.a112510.

    +
    +
    +

    Silveira, Mariângela F., Aluı́sio J. D. Barros, Bernardo L. Horta, Lúcia C. Pellanda, Gabriel D. Victora, Odir A. Dellagostin, Claudio J. Struchiner, et al. 2020. “Population-Based Surveys of Antibodies Against SARS-CoV-2 in Southern Brazil.” Nature Medicine 26 (8): 1196–9. https://doi.org/10.1038/s41591-020-0992-3.

    +
    +
    +

    Takahashi, Saki, Bryan Greenhouse, and Isabel Rodríguez-Barraquer. 2020. “Are SARS-CoV-2 seroprevalence estimates biased?” The Journal of Infectious Diseases, August. https://doi.org/10.1093/infdis/jiaa523.

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  • Workflow: How to use the serosurvey R package?
    • +
  • + Introduction: serosurvey R package +
    • diff --git a/docs/index.html b/docs/index.html index f5a3217..9cf1f67 100644 --- a/docs/index.html +++ b/docs/index.html @@ -59,6 +59,9 @@
  • Workflow: How to use the serosurvey R package?
    • +
  • + Introduction: serosurvey R package +
    • @@ -81,6 +84,9 @@
    + +


    Disclaimer

    +

    This package is a work in progress. It has been released to get feedback from users that we can incorporate in future releases.

    serosurvey

    @@ -91,221 +97,33 @@

    Installation

    +

    You can install the developmental version of serosurvey from GitHub with:

    if(!require("remotes")) install.packages("remotes")
 remotes::install_github("avallecam/serosurvey")
    -
    +

    -Example

    -

    Three basic examples which shows you how to solve common problems:

    -
    library(serosurvey)
    -
    -

    -1. survey: Estimate single prevalences

    -
      +Brief description

    +

    The current workflow is divided in two steps:

    +
    1. -

      From a srvyr survey design object, serosvy_proportion estimates:

      -
        -
      • weighted prevalence (prop),
        • -
      • total population (total),
        • -
      • raw proportion (raw_prop),
        • -
      • coefficient of variability (cv),
        • -
      • design effect (deff)
        • -
      -
      2. - - -
      example("serosvy_proportion")
      - -
      -

      -2. survey: Estimate multiple prevalences

      -
        +survey: Estimate multiple prevalences, and
      • -

        In the Article tab we provide a workflow to estimate multiple prevalences:

        -
          -
        • using different set of covariates and outcomes as numerators or denominators,
          • -
        • in one single pipe operation
          • -
        -
        • -
      -
      # crear matriz
-  #
-  # set 01 of denominator-numerator
-  #
-expand_grid(
-  design=list(design),
-  denominator=c("covariate_01","covariate_02"), # covariates
-  numerator=c("outcome_one","outcome_two") # outcomes
-  ) %>% 
-  #
-  # set 02 of denominator-numerator (e.g. within main outcome)
-  #
-  union_all(
-    expand_grid(
-      design=list(design),
-      denominator=c("outcome_one","outcome_two"), # outcomes
-      numerator=c("covariate_02") # covariates
-    )
-  ) %>% 
-  #
-  # create symbols (to be readed as arguments)
-  #
-  mutate(
-    denominator=map(denominator,dplyr::sym),
-    numerator=map(numerator,dplyr::sym)
-  ) %>% 
-  #
-  # estimate prevalence
-  #
-  mutate(output=pmap(.l = select(.,design,denominator,numerator),
-                     .f = serosvy_proportion)) %>% 
-  #
-  # show the outcome
-  #
-  select(-design,-denominator,-numerator) %>% 
-  unnest(cols = c(output)) %>% 
-  print(n=Inf)
-#> # A tibble: 25 x 23
-#>    denominator denominator_lev~ numerator numerator_level   prop prop_low
-#>    <chr>       <fct>            <chr>     <fct>            <dbl>    <dbl>
-#>  1 covariate_~ E                outcome_~ No              0.211   0.130  
-#>  2 covariate_~ E                outcome_~ Yes             0.789   0.675  
-#>  3 covariate_~ H                outcome_~ No              0.852   0.564  
-#>  4 covariate_~ H                outcome_~ Yes             0.148   0.0377 
-#>  5 covariate_~ M                outcome_~ No              0.552   0.224  
-#>  6 covariate_~ M                outcome_~ Yes             0.448   0.160  
-#>  7 covariate_~ E                outcome_~ (-0.1,50]       0.182   0.0499 
-#>  8 covariate_~ E                outcome_~ (50,100]        0.818   0.515  
-#>  9 covariate_~ H                outcome_~ (-0.1,50]       0.0769  0.00876
-#> 10 covariate_~ H                outcome_~ (50,100]        0.923   0.560  
-#> 11 covariate_~ M                outcome_~ (50,100]        1.00    1.00   
-#> 12 covariate_~ No               outcome_~ No              1.00    1.00   
-#> 13 covariate_~ Yes              outcome_~ No              0.0334  0.00884
-#> 14 covariate_~ Yes              outcome_~ Yes             0.967   0.882  
-#> 15 covariate_~ No               outcome_~ (-0.1,50]       0.218   0.0670 
-#> 16 covariate_~ No               outcome_~ (50,100]        0.782   0.479  
-#> 17 covariate_~ Yes              outcome_~ (-0.1,50]       0.0914  0.0214 
-#> 18 covariate_~ Yes              outcome_~ (50,100]        0.909   0.684  
-#> 19 outcome_one No               covariat~ No              0.939   0.778  
-#> 20 outcome_one No               covariat~ Yes             0.0615  0.0148 
-#> 21 outcome_one Yes              covariat~ Yes             1.00    1.00   
-#> 22 outcome_two (-0.1,50]        covariat~ No              0.549   0.294  
-#> 23 outcome_two (-0.1,50]        covariat~ Yes             0.451   0.219  
-#> 24 outcome_two (50,100]         covariat~ No              0.305   0.188  
-#> 25 outcome_two (50,100]         covariat~ Yes             0.695   0.546  
-#> # ... with 17 more variables: prop_upp <dbl>, prop_cv <dbl>,
-#> #   prop_se <dbl>, total <dbl>, total_low <dbl>, total_upp <dbl>,
-#> #   total_cv <dbl>, total_se <dbl>, total_deff <dbl>, total_den <dbl>,
-#> #   total_den_low <dbl>, total_den_upp <dbl>, raw_num <int>,
-#> #   raw_den <int>, raw_prop <dbl>, raw_prop_low <dbl>, raw_prop_upp <dbl>
      -
      -
      -

      -3. serology: Estimate prevalence Under misclassification

      -
        -

      • We gather one frequentist approach (Rogan and Gladen 1978), available in different Github repos, that deal with misclassification due to an imperfect diagnostic test (Azman et al. 2020; Takahashi, Greenhouse, and Rodríguez-Barraquer 2020). Check the Reference tab.

        • -

      • We provide tidy outputs for bayesian approaches developed in Daniel B. Larremore et al. (2020) here and Daniel B Larremore et al. (2020) here:

        • -

      • You can use them with purrr and furrr to efficiently iterate and parallelize this step for multiple prevalences. Check the workflow in Article tab.

        • -
      -
      -

      -Known test performance - Bayesian method -

      - -

      -
      example("serosvy_known_sample_posterior")
      +serology: Estimate prevalence Under misclassification for a device with Known or Unknown test performance +
    -
    -

    -Unknown test performance - Bayesian method -

    +
    +

    +More

      -
    • The test performance is called “unknown” or “uncertain” when test sensitivity and specificity are not known with certainty (Kritsotakis 2020; Diggle 2011; Gelman and Carpenter 2020) and lab validation data is available with a limited set of samples, tipically during a novel pathogen outbreak.
      • +
    • In the Introductory article we provide detailed definitions and references of the methods available.
      • +
    • In the Workflow article we provide a reproducible example with this package.
    - -

    -
    example("serosvy_unknown_sample_posterior")
    -
    -
    - -
    -

    -Run a learnr tutorial

    -
    # install package
-if(!require("remotes")) install.packages("remotes")
-remotes::install_github("avallecam/serosurvey")
-# install learner and run tutorial
-if(!require("learnr")) install.packages("learnr")
-learnr::run_tutorial(name = "taller",package = "serosurvey")

    Contributing

    Feel free to fill an issue or contribute with your functions or workflows in a pull request.

    -

    Here are a list of publications with interesting approaches using R:

    -
      -

    • Silveira et al. (2020) and Hallal et al. (2020) analysed a serological survey accounting for sampling design and test validity using parametric bootstraping, following Lewis and Torgerson (2012).

      • -

    • Flor et al. (2020) implemented a lot of frequentist and bayesian methods for test with known sensitivity and specificity. Code is available here.

      • -

    • Gelman and Carpenter (2020) also applied Bayesian inference with hierarchical regression and post-stratification to account for test uncertainty with unknown specificity and sensitivity. Here a case-study.

      • -
    -
    -

    @@ -318,147 +136,34 @@

    Acknowledgements

    Many thanks to the Centro Nacional de Epidemiología, Prevención y Control de Enfermedades (CDC Perú) for the opportunity to work on this project.

    -
    +

    -References

    -

    Azman, Andrew S, Stephen Lauer, M. Taufiqur Rahman Bhuiyan, Francisco J Luquero, Daniel T Leung, Sonia Hegde, Jason B Harris, et al. 2020. “Vibrio Cholerae O1 Transmission in Bangladesh: Insights from a Nationally- Representative Serosurvey,” March. https://doi.org/10.1101/2020.03.13.20035352.

    -

    Diggle, Peter J. 2011. “Estimating Prevalence Using an Imperfect Test.” Epidemiology Research International 2011: 1–5. https://doi.org/10.1155/2011/608719.

    -

    Flor, Matthias, Michael Weiß, Thomas Selhorst, Christine Müller-Graf, and Matthias Greiner. 2020. “Comparison of Bayesian and Frequentist Methods for Prevalence Estimation Under Misclassification.” BMC Public Health 20 (1). https://doi.org/10.1186/s12889-020-09177-4.

    -

    Gelman, Andrew, and Bob Carpenter. 2020. “Bayesian Analysis of Tests with Unknown Specificity and Sensitivity.” Journal of the Royal Statistical Society: Series C (Applied Statistics), August. https://doi.org/10.1111/rssc.12435.

    -

    Hallal, Pedro C, Fernando P Hartwig, Bernardo L Horta, Mariângela F Silveira, Claudio J Struchiner, Luı́s P Vidaletti, Nelson A Neumann, et al. 2020. “SARS-CoV-2 Antibody Prevalence in Brazil: Results from Two Successive Nationwide Serological Household Surveys.” The Lancet Global Health, September. https://doi.org/10.1016/s2214-109x(20)30387-9.

    -

    Kritsotakis, Evangelos I. 2020. “On the Importance of Population-Based Serological Surveys of SARS-CoV-2 Without Overlooking Their Inherent Uncertainties.” Public Health in Practice 1 (November): 100013. https://doi.org/10.1016/j.puhip.2020.100013.

    -

    Larremore, Daniel B., Bailey K Fosdick, Kate M Bubar, Sam Zhang, Stephen M Kissler, C. Jessica E. Metcalf, Caroline Buckee, and Yonatan Grad.2020.“Estimating SARS-CoV-2 Seroprevalence and Epidemiological Parameters with Uncertainty from Serological Surveys.” medRxiv, April. https://doi.org/10.1101/2020.04.15.20067066.

    -

    Larremore, Daniel B., Bailey K. Fosdick, Sam Zhang, and Yonatan Grad.2020.“Jointly Modeling Prevalence, Sensitivity and Specificity for Optimal Sample Allocation.” bioRxiv, May. https://doi.org/10.1101/2020.05.23.112649.

    -

    Lewis, Fraser I, and Paul R Torgerson. 2012. “A Tutorial in Estimating the Prevalence of Disease in Humans and Animals in the Absence of a Gold Standard Diagnostic.” Emerging Themes in Epidemiology 9 (1). https://doi.org/10.1186/1742-7622-9-9.

    -

    Rogan, Walter J., and Beth Gladen. 1978. “Estimating Prevalence from the Results of A Screening Test.” American Journal of Epidemiology 107 (1): 71–76. https://doi.org/10.1093/oxfordjournals.aje.a112510.

    -

    Silveira, Mariângela F., Aluı́sio J. D. Barros, Bernardo L. Horta, Lúcia C. Pellanda, Gabriel D. Victora, Odir A. Dellagostin, Claudio J. Struchiner, et al. 2020. “Population-Based Surveys of Antibodies Against SARS-CoV-2 in Southern Brazil.” Nature Medicine 26 (8): 1196–9. https://doi.org/10.1038/s41591-020-0992-3.

    -

    Takahashi, Saki, Bryan Greenhouse, and Isabel Rodríguez-Barraquer. 2020. “Are SARS-CoV-2 seroprevalence estimates biased?” The Journal of Infectious Diseases, August. https://doi.org/10.1093/infdis/jiaa523.

    -
    - -
    - -Azman, Andrew S, Stephen Lauer, M. Taufiqur Rahman Bhuiyan, Francisco J -Luquero, Daniel T Leung, Sonia Hegde, Jason B Harris, et al. 2020. -“Vibrio Cholerae O1 Transmission in Bangladesh: Insights from a -Nationally- Representative Serosurvey,” March. -. - - -
    - -
    - -Diggle, Peter J. 2011. “Estimating Prevalence Using an Imperfect Test.” -*Epidemiology Research International* 2011: 1–5. -. - - -
    - -
    - -Flor, Matthias, Michael Weiß, Thomas Selhorst, Christine Müller-Graf, -and Matthias Greiner. 2020. “Comparison of Bayesian and Frequentist -Methods for Prevalence Estimation Under Misclassification.” *BMC Public -Health* 20 (1). . - - -
    - -
    - -Gelman, Andrew, and Bob Carpenter. 2020. “Bayesian Analysis of Tests -with Unknown Specificity and Sensitivity.” *Journal of the Royal -Statistical Society: Series C (Applied Statistics)*, August. -. - - -
    - -
    - -Hallal, Pedro C, Fernando P Hartwig, Bernardo L Horta, Mariângela F -Silveira, Claudio J Struchiner, Luı́s P Vidaletti, Nelson A Neumann, et -al. 2020. “SARS-CoV-2 Antibody Prevalence in Brazil: Results from Two -Successive Nationwide Serological Household Surveys.” *The Lancet Global -Health*, September. . - - -
    - -
    - -Kritsotakis, Evangelos I. 2020. “On the Importance of Population-Based -Serological Surveys of SARS-CoV-2 Without Overlooking Their Inherent -Uncertainties.” *Public Health in Practice* 1 (November): 100013. -. - - -
    - -
    - -Larremore, Daniel B, Bailey K Fosdick, Kate M Bubar, Sam Zhang, Stephen -M Kissler, C. Jessica E. Metcalf, Caroline Buckee, and Yonatan Grad. -2020. “Estimating SARS-CoV-2 Seroprevalence and Epidemiological -Parameters with Uncertainty from Serological Surveys.” *medRxiv*, April. -. - - -
    - -
    - -Larremore, Daniel B., Bailey K. Fosdick, Sam Zhang, and Yonatan H. Grad. -2020. “Jointly Modeling Prevalence, Sensitivity and Specificity for -Optimal Sample Allocation.” *bioRxiv*, May. -. - - -
    - -
    - -Lewis, Fraser I, and Paul R Torgerson. 2012. “A Tutorial in Estimating -the Prevalence of Disease in Humans and Animals in the Absence of a Gold -Standard Diagnostic.” *Emerging Themes in Epidemiology* 9 (1). -. - - -
    - -
    - -Rogan, Walter J., and Beth Gladen. 1978. “Estimating Prevalence from the -Results of A Screening Test.” *American Journal of Epidemiology* 107 -(1): 71–76. . - - -
    - - - -
    - -Takahashi, Saki, Bryan Greenhouse, and Isabel Rodríguez-Barraquer. 2020. -“Are SARS-CoV-2 seroprevalence estimates biased?” *The Journal of -Infectious Diseases*, August. . - -
    -
    -
    -
    - 
    ggplot_prevalence(data, category, outcome, proportion, proportion_upp,
-  proportion_low, breaks_n = 5)
-
-ggplot_prevalence_ii(data, denominator_level, numerator, proportion,
-  proportion_upp, proportion_low, breaks_n = 5)
    + 
    ggplot_prevalence(data, denominator_level, numerator, proportion,
+  proportion_upp, proportion_low)

    Arguments

    @@ -140,12 +140,16 @@

    Arg

    - - + + - - + + @@ -157,23 +161,8 @@

    Arg

    - - - - - - - - - - - - - +

    input tibble
    category

    denominator level

    		denominator_level
      +

    • denominator values column

      • +
    outcome

    numerator variable

    		numerator
      +

    • numerator variable name column

      • +
    proportion
    proportion_low

    lower interval

    breaks_n

    number of breaks in axis

    denominator_level
      -

    • denominator values column

      • -
    numerator
      -

    • numerator variable name column

      • -

    lower interval +#param breaks_n number of breaks in axis
    @@ -182,7 +171,6 @@

    Fun

    • ggplot_prevalence: ggplot2 visualization of proportions

      • -

    • ggplot_prevalence_ii: ggplot_prevalence with new arguments

    diff --git a/docs/reference/index.html b/docs/reference/index.html index a8f4f01..8453798 100644 --- a/docs/reference/index.html +++ b/docs/reference/index.html @@ -88,6 +88,9 @@
  • Workflow: How to use the serosurvey R package?
    • +
  • + Introduction: serosurvey R package +
    • @@ -139,7 +142,7 @@

    ggplot_prevalence() ggplot_prevalence_ii()

    +

    ggplot_prevalence()

    Visualization of proportions

    diff --git a/docs/reference/rogan_gladen_stderr_unk.html b/docs/reference/rogan_gladen_stderr_unk.html index 372364c..fbc602b 100644 --- a/docs/reference/rogan_gladen_stderr_unk.html +++ b/docs/reference/rogan_gladen_stderr_unk.html @@ -91,6 +91,9 @@
  • Workflow: How to use the serosurvey R package?
    • +
  • + Introduction: serosurvey R package +
    • diff --git a/docs/reference/sample_posterior_r_mcmc_hyperR.html b/docs/reference/sample_posterior_r_mcmc_hyperR.html index 449b760..f022e15 100644 --- a/docs/reference/sample_posterior_r_mcmc_hyperR.html +++ b/docs/reference/sample_posterior_r_mcmc_hyperR.html @@ -91,6 +91,9 @@
  • Workflow: How to use the serosurvey R package?
    • +
  • + Introduction: serosurvey R package +
    • diff --git a/docs/reference/sample_posterior_r_mcmc_testun.html b/docs/reference/sample_posterior_r_mcmc_testun.html index a3a4f5e..d9da228 100644 --- a/docs/reference/sample_posterior_r_mcmc_testun.html +++ b/docs/reference/sample_posterior_r_mcmc_testun.html @@ -91,6 +91,9 @@
  • Workflow: How to use the serosurvey R package?
    • +
  • + Introduction: serosurvey R package +
    • diff --git a/docs/reference/serosvy_known_sample_posterior.html b/docs/reference/serosvy_known_sample_posterior.html index e194ce0..675cbea 100644 --- a/docs/reference/serosvy_known_sample_posterior.html +++ b/docs/reference/serosvy_known_sample_posterior.html @@ -91,6 +91,9 @@
  • Workflow: How to use the serosurvey R package?
    • +
  • + Introduction: serosurvey R package +
    • diff --git a/docs/reference/serosvy_unknown_sample_posterior.html b/docs/reference/serosvy_unknown_sample_posterior.html index 62316cf..221e643 100644 --- a/docs/reference/serosvy_unknown_sample_posterior.html +++ b/docs/reference/serosvy_unknown_sample_posterior.html @@ -91,6 +91,9 @@
  • Workflow: How to use the serosurvey R package?
    • +
  • + Introduction: serosurvey R package +
    • diff --git a/docs/reference/srvyr_prop_step_01.html b/docs/reference/srvyr_prop_step_01.html index c527187..1ac491e 100644 --- a/docs/reference/srvyr_prop_step_01.html +++ b/docs/reference/srvyr_prop_step_01.html @@ -91,6 +91,9 @@
  • Workflow: How to use the serosurvey R package?
    • +
  • + Introduction: serosurvey R package +
    • diff --git a/docs/reference/unite_dotwhiskers.html b/docs/reference/unite_dotwhiskers.html index 1412cd1..7acf19d 100644 --- a/docs/reference/unite_dotwhiskers.html +++ b/docs/reference/unite_dotwhiskers.html @@ -91,6 +91,9 @@
  • Workflow: How to use the serosurvey R package?
    • +
  • + Introduction: serosurvey R package +