From 21174d0a5c2ab1a2fcf44137f7cf2fb50f541eb5 Mon Sep 17 00:00:00 2001 From: Thomas Debray <117118104+tdebray123@users.noreply.github.com> Date: Fri, 24 Nov 2023 19:57:51 +0100 Subject: [PATCH] add chapter authors --- .github/workflows/publish.yml | 6 +- _freeze/chapter_11/execute-results/html.json | 4 +- .../figure-html/unnamed-chunk-6-1.png | Bin 48727 -> 63303 bytes .../figure-html/unnamed-chunk-7-1.png | Bin 68114 -> 48727 bytes .../figure-html/unnamed-chunk-8-1.png | Bin 0 -> 68114 bytes authors.qmd | 17 +- .../figure-html/unnamed-chunk-6-1.png | Bin 48727 -> 63303 bytes .../figure-html/unnamed-chunk-7-1.png | Bin 68114 -> 48727 bytes .../figure-html/unnamed-chunk-8-1.png | Bin 0 -> 68114 bytes docs/authors.html | 539 ++++++++++++++++++ docs/chapter_11.html | 74 +-- .../figure-html/unnamed-chunk-6-1.png | Bin 48727 -> 63303 bytes .../figure-html/unnamed-chunk-7-1.png | Bin 68114 -> 48727 bytes .../figure-html/unnamed-chunk-8-1.png | Bin 0 -> 68114 bytes docs/resources/orcid.png | Bin 0 -> 38257 bytes docs/search.json | 18 +- resources/chapter 12/fig_functions.r | 2 - resources/orcid.png | Bin 0 -> 38257 bytes 18 files changed, 616 insertions(+), 44 deletions(-) create mode 100644 _freeze/chapter_11/figure-html/unnamed-chunk-8-1.png create mode 100644 chapter_11_files/figure-html/unnamed-chunk-8-1.png create mode 100644 docs/authors.html create mode 100644 docs/chapter_11_files/figure-html/unnamed-chunk-8-1.png create mode 100644 docs/resources/orcid.png create mode 100644 resources/orcid.png diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index d341574..858c2d5 100644 --- a/.github/workflows/publish.yml +++ b/.github/workflows/publish.yml @@ -60,10 +60,12 @@ jobs: any::ggplot2 any::ggpubr any::gridExtra + any::jarbes any::jomo any::kableExtra any::knitr any::MatchThem + any::Matrix any::metafor any::mice any::missForest @@ -81,14 +83,13 @@ jobs: any::rpart.plot any::sandwich any::sparseMVN + any::survey any::tidyverse any::table1 any::tableone - name: Install remaining R packages run: | - Rscript -e 'install.packages("Matrix", dependencies=TRUE)' - Rscript -e 'install.packages("survey", dependencies=TRUE)' Rscript -e 'install.packages("MatchIt", dependencies=TRUE)' Rscript -e 'install.packages("WeightIt", dependencies=TRUE)' Rscript -e 'install.packages("optmatch", dependencies=TRUE)' @@ -96,7 +97,6 @@ jobs: Rscript -e 'install.packages("officer", dependencies=TRUE)' Rscript -e 'install.packages("testthat", dependencies=TRUE)' Rscript -e 'install.packages("interactionR", dependencies=TRUE)' - Rscript -e 'install.packages("jarbes", dependencies=TRUE)' - name: Render and Publish uses: quarto-dev/quarto-actions/publish@v2 diff --git a/_freeze/chapter_11/execute-results/html.json b/_freeze/chapter_11/execute-results/html.json index 9f1f44b..0c95d10 100644 --- a/_freeze/chapter_11/execute-results/html.json +++ b/_freeze/chapter_11/execute-results/html.json @@ -1,7 +1,7 @@ { - "hash": "12fb8d715d1d2ead348f6ec7153a3975", + "hash": "169de5af55a5a234079f68302aedb93c", "result": { - "markdown": "---\ntitle: \"Individual Participant Data Meta-analysis of clinical trials and real-world data\"\nauthors: \n - name: Pablo Verde\n affiliations:\n - ref: umcdusseldorf\n - name: Thomas Debray\n orcid: 0000-0002-1790-2719\n affiliations:\n - ref: smartdas\naffiliations:\n - id: smartdas\n name: Smart Data Analysis and Statistics B.V.\n city: Utrecht\n - id: umcdusseldorf\n name: Universitätsklinikum Düsseldorf\n city: Düsseldorf\nformat:\n html:\n toc: true\n number-sections: true\nexecute:\n cache: true\nbibliography: 'https://api.citedrive.com/bib/0d25b38b-db8f-43c4-b934-f4e2f3bd655a/references.bib?x=eyJpZCI6ICIwZDI1YjM4Yi1kYjhmLTQzYzQtYjkzNC1mNGUyZjNiZDY1NWEiLCAidXNlciI6ICIyNTA2IiwgInNpZ25hdHVyZSI6ICI0MGFkYjZhMzYyYWE5Y2U0MjQ2NWE2ZTQzNjlhMWY3NTk5MzhhNzUxZDNjYWIxNDlmYjM4NDgwOTYzMzY5YzFlIn0=/bibliography.bib'\n---\n\n\n\n\n## Introduction\n\n\n## Hierarchical Meta-Regression\nWe illustrate the implementation of hierarchical meta-regression using an example that involves the following data sources:\n\n* Aggregate data from 35 randomized trials investigating the efficacy of adjunctive treatments in managing diabetic foot problems compared with routine care\n* Individual participant data from a prospective cohort study investigating patient and limb survival in patients with diabetic foot ulcers \n\n### Aggregate data\nWe first retrieve the randomized evidence and summarize the treatment effect estimates using a random effects meta-analysis:\n\n\n::: {.cell hash='chapter_11_cache/html/unnamed-chunk-2_f7cacc4b2068e8e0b49c42b0d14818e2'}\n\n```{.r .cell-code}\nlibrary(dplyr)\nlibrary(jarbes)\nlibrary(meta)\n\ndata(\"healing\")\n\nresults.ADJ <- metabin(event.c = y_c, n.c = n_c,\n event.e = y_t, n.e = n_t,\n studlab = Study, data = healing,\n sm = \"OR\", \n prediction = TRUE)\n```\n:::\n\n\nThe corresponding forest plot is depicted below. The endpoint is healing without amputations within a period less than or equal to 1 year.\n\n\n::: {.cell hash='chapter_11_cache/html/unnamed-chunk-3_ccb46ff31b59d1bcdf1839864e34eeb2'}\n::: {.cell-output-display}\n![](chapter_11_files/figure-html/unnamed-chunk-3-1.png){width=960}\n:::\n:::\n\n\nThe random effects meta-analysis yielded a pooled odds ratio of 1.90. However, substantial between-study heterogeneity was found, with $\\tau$ = 0.46.\n\n### Individual participant data\nSubsequently, we retrieve the individual participant data:\n\n\n::: {.cell hash='chapter_11_cache/html/unnamed-chunk-4_98abfdc985b108064e02e2f47ea3d856'}\n\n```{.r .cell-code}\ndata(\"healingipd\")\nIPD <- healingipd %>% dplyr::select(healing.without.amp, PAD, neuropathy,\n first.ever.lesion, no.continuous.care, \n male, diab.typ2, insulin, HOCHD, \n HOS, CRF, dialysis, DNOAP, smoking.ever, \n diabdur, wagner.class)\n```\n:::\n\n\nBriefly, these IPD were obtained from a prospective cohort study enrolling consecutive patients with diabetic foot ulcers (DFUs) and without previous major amputation in a single diabetes center between June 1998 and December 1999 [@morbach_long-term_2012]. The baseline characteristics of the study population are summarized below:\n\n\n::: {.cell hash='chapter_11_cache/html/unnamed-chunk-5_a3fa5d48e7c6dee59e8f08278511b2b8'}\n::: {.cell-output-display}\n```{=html}\n
\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Healing without amputation
(N=165)		No healing without amputation
(N=95)		Overall
(N=260)
Age (years)
Mean (SD)69.1 (10.9)68.5 (11.0)68.9 (10.9)
Median [Min, Max]70.0 [25.0, 90.0]69.0 [36.0, 91.0]70.0 [25.0, 91.0]
Diabetes duration (years)
Mean (SD)15.9 (10.3)15.9 (11.2)15.9 (10.6)
Median [Min, Max]14.0 [1.00, 53.0]14.0 [0, 50.0]14.0 [0, 53.0]
Sex
Female71 (43.0%)35 (36.8%)106 (40.8%)
Male94 (57.0%)60 (63.2%)154 (59.2%)
Ever smoker
Yes97 (58.8%)57 (60.0%)154 (59.2%)
No68 (41.2%)38 (40.0%)106 (40.8%)
Diabetes type 2
Yes150 (90.9%)79 (83.2%)229 (88.1%)
No15 (9.1%)16 (16.8%)31 (11.9%)
Peripheral arterial disease
Yes82 (49.7%)66 (69.5%)148 (56.9%)
No83 (50.3%)29 (30.5%)112 (43.1%)
Neuropathy
Yes144 (87.3%)80 (84.2%)224 (86.2%)
No21 (12.7%)15 (15.8%)36 (13.8%)
First ever lesion
Yes70 (42.4%)44 (46.3%)114 (43.8%)
No95 (57.6%)51 (53.7%)146 (56.2%)
No continuous care
Yes115 (69.7%)62 (65.3%)177 (68.1%)
No50 (30.3%)33 (34.7%)83 (31.9%)
Insulin dependent
Yes109 (66.1%)65 (68.4%)174 (66.9%)
No56 (33.9%)30 (31.6%)86 (33.1%)
History of coronary events (CHD)
Yes31 (18.8%)21 (22.1%)52 (20.0%)
No134 (81.2%)74 (77.9%)208 (80.0%)
History of stroke
Yes36 (21.8%)19 (20.0%)55 (21.2%)
No129 (78.2%)76 (80.0%)205 (78.8%)
Charcot foot syndrome
Yes28 (17.0%)24 (25.3%)52 (20.0%)
No137 (83.0%)71 (74.7%)208 (80.0%)
Dialysis
Yes3 (1.8%)6 (6.3%)9 (3.5%)
No162 (98.2%)89 (93.7%)251 (96.5%)
DNOAP
Yes19 (11.5%)10 (10.5%)29 (11.2%)
No146 (88.5%)85 (89.5%)231 (88.8%)
Wagner score
1-2115 (69.7%)27 (28.4%)142 (54.6%)
3-4-550 (30.3%)68 (71.6%)118 (45.4%)
\n
\n```\n:::\n:::\n\n\nAs depicted above, IPD are available from 260 patients. Some of these patients have similar characteristics to those enrolled in the randomized trials. However, other patients have comorbidities, where one or more risk factors prevent them to participate in the RCTs due to ethical reasons. For example,\n118 patients have severe ulcer lesions (Wagner score 3 to 5), and 77 patients suffer from severe ulcer lesions and peripheral arterial disease (PAD). The question is: Can we generalize the benefit of adjuvant therapies observed in the RCTs to the subgroups of patients encountered in clinical practice?\n\n### Hierarchical metaregression\nWe fitted an HMR model to the available RWD and published AD: \n\n\n::: {.cell hash='chapter_11_cache/html/hmr_fit_2740671afa49efcae98d1899c9eb7d7e'}\n\n```{.r .cell-code}\nset.seed(2022)\n\nAD <- healing %>% dplyr::select(yc = y_c, nc = n_c, \n yt = y_t, nt = n_t, Study = Study)\n\nmx2 <- hmr(data = AD, # Published aggregate data\n two.by.two = FALSE, # \n dataIPD = IPD, # Data frame of the IPD \n re = \"sm\", # Random effects model: \"sm\" scale mixtures \n link = \"logit\", # Link function of the random effects\n sd.mu.1 = 1, # Scale parameter for the prior of mu.1\n sd.mu.2 = 1, # Scale parameter for the prior of mu.2 \n sd.mu.phi = 1, # Scale parameter for the prior of mu.phi \n sigma.1.upper = 5, # Upper bound of the prior of sigma.1 \n sigma.2.upper = 5, # Upper bound of the prior of sigma.2\n sigma.beta.upper = 5, # Upper bound of the prior of sigma.beta\n sd.Fisher.rho = 1.25, # Scale parameter for the prior of rho\n df.estimate = TRUE, # If TRUE the degrees of freedom are estimated\n df.lower = 3, # Lower bound of the df's prior\n df.upper = 10, # Upper bound of the df's prior\n nr.chains = 2, # Number of MCMC chains\n nr.iterations = 10000, # Total number of iterations\n nr.adapt = 1000, # Number of iteration for burnin \n nr.thin = 1) # Thinning rate\n```\n:::\n\n\nWe start our analysis by visualizing the conflict of evidence between the different types of data and study types. The figure below depicts the posterior distribution of $\\mu_{\\phi}$, which is the mean bias of the IPD-NRS compared to the AD-RCTs control groups. The posterior distribution has a substantial probability mass on the right of zero, which indicates that in average the IPD-NRS patients present a better prognoses than the AD-RCTs control groups. That means that taking the IPD-NRS results at face value would be misleading if we aim to combine them with a meta-analysis of AD-RCTs.\n\n\n\n::: {.cell hash='chapter_11_cache/html/unnamed-chunk-6_34ac7ace045f4a230f5a3d0b1500214c'}\n::: {.cell-output-display}\n![Conflict of evidence analysis. The left panel shows the prior to posterior sensitivity analysis of bias mean between the RCTs and the IPD-NRS. The right panel depicts the posterior distribution of the outliers detection weights.](chapter_11_files/figure-html/unnamed-chunk-6-1.png){width=1056}\n:::\n:::\n\n\n\nThe figure below presents the posterior distribution of the weights $w_{i}$ for each study included in the HMR. These posteriors are summarized using a forest plot, where posterior intervals substantially greater than one indicate outliers. One important aspect of the HMR is that those outliers are automatically down-weighted in the analysis.\n\n::: {.cell hash='chapter_11_cache/html/unnamed-chunk-7_7bf6c90185feaf660fc83a48724970b6'}\n::: {.cell-output-display}\n![Posterior distribution of the weights for each study included in the HMR](chapter_11_files/figure-html/unnamed-chunk-7-1.png){width=1056}\n:::\n:::\n\n\n\n## Version info {.unnumbered}\nThis chapter was rendered using the following version of R and its packages:\n\n\n::: {.cell hash='chapter_11_cache/html/unnamed-chunk-8_097e9efc1fec5df7c7a25885223b5cef'}\n::: {.cell-output .cell-output-stdout}\n```\nR version 4.2.3 (2023-03-15 ucrt)\nPlatform: x86_64-w64-mingw32/x64 (64-bit)\nRunning under: Windows 10 x64 (build 19045)\n\nMatrix products: default\n\nlocale:\n[1] LC_COLLATE=Dutch_Netherlands.utf8 LC_CTYPE=Dutch_Netherlands.utf8 \n[3] LC_MONETARY=Dutch_Netherlands.utf8 LC_NUMERIC=C \n[5] LC_TIME=Dutch_Netherlands.utf8 \n\nattached base packages:\n[1] stats graphics grDevices utils datasets methods base \n\nother attached packages:\n [1] meta_6.5-0 table1_1.4.3 tableone_0.13.2 dplyr_1.1.2 \n [5] jarbes_2.0.0 GGally_2.1.2 R2jags_0.7-1 rjags_4-14 \n [9] mcmcplots_0.4.3 coda_0.19-4 gridExtra_2.3 ggplot2_3.4.4 \n[13] kableExtra_1.3.4\n\nloaded via a namespace (and not attached):\n [1] httr_1.4.7 tidyr_1.3.0 sfsmisc_1.1-16 \n [4] jsonlite_1.8.7 viridisLite_0.4.2 splines_4.2.3 \n [7] Formula_1.2-5 shiny_1.8.0 metafor_4.4-0 \n[10] yaml_2.3.7 numDeriv_2016.8-1.1 R2WinBUGS_2.1-21 \n[13] pillar_1.9.0 lattice_0.21-8 glue_1.6.2 \n[16] digest_0.6.31 RColorBrewer_1.1-3 promises_1.2.1 \n[19] minqa_1.2.6 rvest_1.0.3 colorspace_2.1-0 \n[22] htmltools_0.5.5 httpuv_1.6.12 Matrix_1.5-4.1 \n[25] survey_4.2-1 plyr_1.8.8 pkgconfig_2.0.3 \n[28] purrr_1.0.1 xtable_1.8-4 scales_1.2.1 \n[31] webshot_0.5.5 svglite_2.1.1 later_1.3.1 \n[34] metadat_1.2-0 lme4_1.1-35.1 tibble_3.2.1 \n[37] generics_0.1.3 ellipsis_0.3.2 withr_2.5.2 \n[40] cli_3.6.1 survival_3.5-5 magrittr_2.0.3 \n[43] mime_0.12 evaluate_0.23 fansi_1.0.4 \n[46] nlme_3.1-162 MASS_7.3-60 xml2_1.3.4 \n[49] tools_4.2.3 mitools_2.4 lifecycle_1.0.4 \n[52] stringr_1.5.1 munsell_0.5.0 compiler_4.2.3 \n[55] systemfonts_1.0.4 rlang_1.1.1 nloptr_2.0.3 \n[58] grid_4.2.3 rstudioapi_0.15.0 CompQuadForm_1.4.3 \n[61] htmlwidgets_1.6.2 miniUI_0.1.1.1 rmarkdown_2.25 \n[64] boot_1.3-28.1 gtable_0.3.4 abind_1.4-5 \n[67] DBI_1.1.3 reshape_0.8.9 R6_2.5.1 \n[70] knitr_1.45 denstrip_1.5.4 fastmap_1.1.1 \n[73] utf8_1.2.3 mathjaxr_1.6-0 ggExtra_0.10.1 \n[76] stringi_1.7.12 parallel_4.2.3 Rcpp_1.0.10 \n[79] vctrs_0.6.3 tidyselect_1.2.0 xfun_0.39 \n```\n:::\n:::\n\n\n## References {.unnumbered}\n\n", + "markdown": "---\ntitle: \"Individual Participant Data Meta-analysis of clinical trials and real-world data\"\nauthors: \n - name: Pablo Verde\n affiliations:\n - ref: umcdusseldorf\n - name: Thomas Debray\n orcid: 0000-0002-1790-2719\n affiliations:\n - ref: smartdas\naffiliations:\n - id: smartdas\n name: Smart Data Analysis and Statistics B.V.\n city: Utrecht\n - id: umcdusseldorf\n name: Universitätsklinikum Düsseldorf\n city: Düsseldorf\nformat:\n html:\n toc: true\n number-sections: true\nexecute:\n cache: true\nbibliography: 'https://api.citedrive.com/bib/0d25b38b-db8f-43c4-b934-f4e2f3bd655a/references.bib?x=eyJpZCI6ICIwZDI1YjM4Yi1kYjhmLTQzYzQtYjkzNC1mNGUyZjNiZDY1NWEiLCAidXNlciI6ICIyNTA2IiwgInNpZ25hdHVyZSI6ICI0MGFkYjZhMzYyYWE5Y2U0MjQ2NWE2ZTQzNjlhMWY3NTk5MzhhNzUxZDNjYWIxNDlmYjM4NDgwOTYzMzY5YzFlIn0=/bibliography.bib'\n---\n\n\n\n\n## Introduction\n\n\n## Hierarchical Meta-Regression\nWe illustrate the implementation of hierarchical meta-regression using an example that involves the following data sources:\n\n* Aggregate data from 35 randomized trials investigating the efficacy of adjunctive treatments in managing diabetic foot problems compared with routine care\n* Individual participant data from a prospective cohort study investigating patient and limb survival in patients with diabetic foot ulcers \n\n### Aggregate data\nWe first retrieve the randomized evidence and summarize the treatment effect estimates using a random effects meta-analysis:\n\n\n::: {.cell hash='chapter_11_cache/html/unnamed-chunk-2_f7cacc4b2068e8e0b49c42b0d14818e2'}\n\n```{.r .cell-code}\nlibrary(dplyr)\nlibrary(jarbes)\nlibrary(meta)\n\ndata(\"healing\")\n\nresults.ADJ <- metabin(event.c = y_c, n.c = n_c,\n event.e = y_t, n.e = n_t,\n studlab = Study, data = healing,\n sm = \"OR\", \n prediction = TRUE)\n```\n:::\n\n\nThe corresponding forest plot is depicted below. The endpoint is healing without amputations within a period less than or equal to 1 year.\n\n\n::: {.cell hash='chapter_11_cache/html/unnamed-chunk-3_ccb46ff31b59d1bcdf1839864e34eeb2'}\n::: {.cell-output-display}\n![](chapter_11_files/figure-html/unnamed-chunk-3-1.png){width=960}\n:::\n:::\n\n\nThe random effects meta-analysis yielded a pooled odds ratio of 1.90. However, substantial between-study heterogeneity was found, with $\\tau$ = 0.46.\n\n### Individual participant data\nSubsequently, we retrieve the individual participant data:\n\n\n::: {.cell hash='chapter_11_cache/html/unnamed-chunk-4_98abfdc985b108064e02e2f47ea3d856'}\n\n```{.r .cell-code}\ndata(\"healingipd\")\nIPD <- healingipd %>% dplyr::select(healing.without.amp, PAD, neuropathy,\n first.ever.lesion, no.continuous.care, \n male, diab.typ2, insulin, HOCHD, \n HOS, CRF, dialysis, DNOAP, smoking.ever, \n diabdur, wagner.class)\n```\n:::\n\n\nBriefly, these IPD were obtained from a prospective cohort study enrolling consecutive patients with diabetic foot ulcers (DFUs) and without previous major amputation in a single diabetes center between June 1998 and December 1999 [@morbach_long-term_2012]. The baseline characteristics of the study population are summarized below:\n\n\n::: {.cell hash='chapter_11_cache/html/unnamed-chunk-5_a3fa5d48e7c6dee59e8f08278511b2b8'}\n::: {.cell-output-display}\n```{=html}\n
\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n
Healing without amputation
(N=165)		No healing without amputation
(N=95)		Overall
(N=260)
Age (years)
Mean (SD)69.1 (10.9)68.5 (11.0)68.9 (10.9)
Median [Min, Max]70.0 [25.0, 90.0]69.0 [36.0, 91.0]70.0 [25.0, 91.0]
Diabetes duration (years)
Mean (SD)15.9 (10.3)15.9 (11.2)15.9 (10.6)
Median [Min, Max]14.0 [1.00, 53.0]14.0 [0, 50.0]14.0 [0, 53.0]
Sex
Female71 (43.0%)35 (36.8%)106 (40.8%)
Male94 (57.0%)60 (63.2%)154 (59.2%)
Ever smoker
Yes97 (58.8%)57 (60.0%)154 (59.2%)
No68 (41.2%)38 (40.0%)106 (40.8%)
Diabetes type 2
Yes150 (90.9%)79 (83.2%)229 (88.1%)
No15 (9.1%)16 (16.8%)31 (11.9%)
Peripheral arterial disease
Yes82 (49.7%)66 (69.5%)148 (56.9%)
No83 (50.3%)29 (30.5%)112 (43.1%)
Neuropathy
Yes144 (87.3%)80 (84.2%)224 (86.2%)
No21 (12.7%)15 (15.8%)36 (13.8%)
First ever lesion
Yes70 (42.4%)44 (46.3%)114 (43.8%)
No95 (57.6%)51 (53.7%)146 (56.2%)
No continuous care
Yes115 (69.7%)62 (65.3%)177 (68.1%)
No50 (30.3%)33 (34.7%)83 (31.9%)
Insulin dependent
Yes109 (66.1%)65 (68.4%)174 (66.9%)
No56 (33.9%)30 (31.6%)86 (33.1%)
History of coronary events (CHD)
Yes31 (18.8%)21 (22.1%)52 (20.0%)
No134 (81.2%)74 (77.9%)208 (80.0%)
History of stroke
Yes36 (21.8%)19 (20.0%)55 (21.2%)
No129 (78.2%)76 (80.0%)205 (78.8%)
Charcot foot syndrome
Yes28 (17.0%)24 (25.3%)52 (20.0%)
No137 (83.0%)71 (74.7%)208 (80.0%)
Dialysis
Yes3 (1.8%)6 (6.3%)9 (3.5%)
No162 (98.2%)89 (93.7%)251 (96.5%)
DNOAP
Yes19 (11.5%)10 (10.5%)29 (11.2%)
No146 (88.5%)85 (89.5%)231 (88.8%)
Wagner score
1-2115 (69.7%)27 (28.4%)142 (54.6%)
3-4-550 (30.3%)68 (71.6%)118 (45.4%)
\n
\n```\n:::\n:::\n\n\nAs depicted above, IPD are available from 260 patients. Some of these patients have similar characteristics to those enrolled in the randomized trials. However, other patients have comorbidities, where one or more risk factors prevent them to participate in the RCTs due to ethical reasons. For example,\n118 patients have severe ulcer lesions (Wagner score 3 to 5), and 77 patients suffer from severe ulcer lesions and peripheral arterial disease (PAD). The question is: Can we generalize the benefit of adjuvant therapies observed in the RCTs to the subgroups of patients encountered in clinical practice?\n\n### Hierarchical metaregression\nWe first investigate the event rate of patients receiving routine care:\n\n\n::: {.cell hash='chapter_11_cache/html/unnamed-chunk-6_299bf59fd3271c56f5ca3cdd40686473'}\n::: {.cell-output-display}\n![](chapter_11_files/figure-html/unnamed-chunk-6-1.png){width=960}\n:::\n:::\n\n\nThe forest plot above indicates that the baseline risk in the observational study from Morbach et al. is much higher than most trials.\n\n\nWe fitted an HMR model to the available RWD and published AD: \n\n\n::: {.cell hash='chapter_11_cache/html/hmr_fit_2740671afa49efcae98d1899c9eb7d7e'}\n\n```{.r .cell-code}\nset.seed(2022)\n\nAD <- healing %>% dplyr::select(yc = y_c, nc = n_c, \n yt = y_t, nt = n_t, Study = Study)\n\nmx2 <- hmr(data = AD, # Published aggregate data\n two.by.two = FALSE, # \n dataIPD = IPD, # Data frame of the IPD \n re = \"sm\", # Random effects model: \"sm\" scale mixtures \n link = \"logit\", # Link function of the random effects\n sd.mu.1 = 1, # Scale parameter for the prior of mu.1\n sd.mu.2 = 1, # Scale parameter for the prior of mu.2 \n sd.mu.phi = 1, # Scale parameter for the prior of mu.phi \n sigma.1.upper = 5, # Upper bound of the prior of sigma.1 \n sigma.2.upper = 5, # Upper bound of the prior of sigma.2\n sigma.beta.upper = 5, # Upper bound of the prior of sigma.beta\n sd.Fisher.rho = 1.25, # Scale parameter for the prior of rho\n df.estimate = TRUE, # If TRUE the degrees of freedom are estimated\n df.lower = 3, # Lower bound of the df's prior\n df.upper = 10, # Upper bound of the df's prior\n nr.chains = 2, # Number of MCMC chains\n nr.iterations = 10000, # Total number of iterations\n nr.adapt = 1000, # Number of iteration for burnin \n nr.thin = 1) # Thinning rate\n```\n:::\n\n\nWe start our analysis by visualizing the conflict of evidence between the different types of data and study types. The figure below depicts the posterior distribution of $\\mu_{\\phi}$, which is the mean bias of the IPD-NRS compared to the AD-RCTs control groups. The posterior distribution has a substantial probability mass below zero, which indicates that in average the IPD-NRS patients present a better prognoses than the AD-RCTs control groups. That means that taking the IPD-NRS results at face value would be misleading if we aim to combine them with a meta-analysis of AD-RCTs.\n\n\n\n::: {.cell hash='chapter_11_cache/html/unnamed-chunk-7_81210f96c1288561a0a3bfea04a90388'}\n::: {.cell-output-display}\n![Conflict of evidence analysis. The left panel shows the prior to posterior sensitivity analysis of bias mean between the RCTs and the IPD-NRS. The right panel depicts the posterior distribution of the outliers detection weights.](chapter_11_files/figure-html/unnamed-chunk-7-1.png){width=1056}\n:::\n:::\n\n\n\nThe figure below presents the posterior distribution of the weights $w_{i}$ for each study included in the HMR. These posteriors are summarized using a forest plot, where posterior intervals substantially greater than one indicate outliers. One important aspect of the HMR is that those outliers are automatically down-weighted in the analysis.\n\n::: {.cell hash='chapter_11_cache/html/unnamed-chunk-8_0ed4f880870d62ff54e258dbf9d747a9'}\n::: {.cell-output-display}\n![Posterior distribution of the weights for each study included in the HMR](chapter_11_files/figure-html/unnamed-chunk-8-1.png){width=1056}\n:::\n:::\n\n\n\n## Version info {.unnumbered}\nThis chapter was rendered using the following version of R and its packages:\n\n\n::: {.cell hash='chapter_11_cache/html/unnamed-chunk-9_f05bdc3d779bf4f97e1fb47a7508a630'}\n::: {.cell-output .cell-output-stdout}\n```\nR version 4.2.3 (2023-03-15 ucrt)\nPlatform: x86_64-w64-mingw32/x64 (64-bit)\nRunning under: Windows 10 x64 (build 19045)\n\nMatrix products: default\n\nlocale:\n[1] LC_COLLATE=Dutch_Netherlands.utf8 LC_CTYPE=Dutch_Netherlands.utf8 \n[3] LC_MONETARY=Dutch_Netherlands.utf8 LC_NUMERIC=C \n[5] LC_TIME=Dutch_Netherlands.utf8 \n\nattached base packages:\n[1] stats graphics grDevices utils datasets methods base \n\nother attached packages:\n [1] meta_6.5-0 table1_1.4.3 tableone_0.13.2 dplyr_1.1.2 \n [5] jarbes_2.0.0 GGally_2.1.2 R2jags_0.7-1 rjags_4-14 \n [9] mcmcplots_0.4.3 coda_0.19-4 gridExtra_2.3 ggplot2_3.4.4 \n[13] kableExtra_1.3.4\n\nloaded via a namespace (and not attached):\n [1] nlme_3.1-162 webshot_0.5.5 RColorBrewer_1.1-3 \n [4] httr_1.4.7 numDeriv_2016.8-1.1 tools_4.2.3 \n [7] utf8_1.2.3 R6_2.5.1 R2WinBUGS_2.1-21 \n[10] metafor_4.4-0 DBI_1.1.3 colorspace_2.1-0 \n[13] withr_2.5.2 tidyselect_1.2.0 compiler_4.2.3 \n[16] cli_3.6.1 rvest_1.0.3 denstrip_1.5.4 \n[19] xml2_1.3.4 labeling_0.4.3 scales_1.2.1 \n[22] sfsmisc_1.1-16 systemfonts_1.0.4 stringr_1.5.1 \n[25] digest_0.6.31 minqa_1.2.6 rmarkdown_2.25 \n[28] svglite_2.1.1 pkgconfig_2.0.3 htmltools_0.5.5 \n[31] lme4_1.1-35.1 fastmap_1.1.1 htmlwidgets_1.6.2 \n[34] rlang_1.1.1 rstudioapi_0.15.0 shiny_1.8.0 \n[37] farver_2.1.1 generics_0.1.3 jsonlite_1.8.7 \n[40] magrittr_2.0.3 Formula_1.2-5 metadat_1.2-0 \n[43] Matrix_1.5-4.1 Rcpp_1.0.10 munsell_0.5.0 \n[46] fansi_1.0.4 abind_1.4-5 lifecycle_1.0.4 \n[49] stringi_1.7.12 yaml_2.3.7 CompQuadForm_1.4.3 \n[52] mathjaxr_1.6-0 MASS_7.3-60 plyr_1.8.8 \n[55] grid_4.2.3 parallel_4.2.3 promises_1.2.1 \n[58] miniUI_0.1.1.1 lattice_0.21-8 splines_4.2.3 \n[61] knitr_1.45 pillar_1.9.0 boot_1.3-28.1 \n[64] codetools_0.2-19 glue_1.6.2 evaluate_0.23 \n[67] mitools_2.4 vctrs_0.6.3 nloptr_2.0.3 \n[70] httpuv_1.6.12 gtable_0.3.4 purrr_1.0.1 \n[73] tidyr_1.3.0 reshape_0.8.9 xfun_0.39 \n[76] ggExtra_0.10.1 mime_0.12 xtable_1.8-4 \n[79] survey_4.2-1 later_1.3.1 survival_3.5-5 \n[82] viridisLite_0.4.2 tibble_3.2.1 ellipsis_0.3.2 \n```\n:::\n:::\n\n\n## References {.unnumbered}\n\n", "supporting": [], "filters": [ "rmarkdown/pagebreak.lua" diff --git a/_freeze/chapter_11/figure-html/unnamed-chunk-6-1.png b/_freeze/chapter_11/figure-html/unnamed-chunk-6-1.png index 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Makady](https://orcid.org/0000-0002-5260-1946) | The Janssen Pharmaceutical Companies of Johnson \& Johnson | Breda| The Netherlands | +| [Christina Read](https://orcid.org/0009-0008-1637-6199) | Utrecht University | Utrecht| The Netherlands | +| [Elvira D'Andrea](https://orcid.org/0000-0002-5263-3964) | AbbVie Inc. | Boston, MA | United States | +| [Grammati Sarri](https://orcid.org/0000-0001-5536-8038) | Cytel Inc. | London| United Kingdom | +| [Jamie Elvidge](https://orcid.org/0000-0002-6154-8091) | National Institute for Health and Care Excellence (NICE) | Manchester| United Kingdom | +| Jeremy Dietz | National Institute for Health and Care Excellence (NICE) | London| United Kingdom | +| [Konstantina Chalkou](https://orcid.org/0000-0001-9718-021X) | Institute of Social and Preventive Medicine (ISPM), University of Bern | Bern| Switzerland | + +: Authors {.hover tbl-colwidths="[20,60,10,10]"} + + diff --git a/chapter_11_files/figure-html/unnamed-chunk-6-1.png 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11  Book Authors

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Thomas Debray

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+ Smart Data Analysis and Statistics B.V. +

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We gratefully acknowledge the contribution from the following authors:

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Authors
AuthorAffiliationCityCountry
Alf ScotlandBiogen Digital Health International GmbHBaarSwitzerland
Amr MakadyThe Janssen Pharmaceutical Companies of Johnson & JohnsonBredaThe Netherlands
Christina ReadUtrecht UniversityUtrechtThe Netherlands
Elvira D’AndreaAbbVie Inc.Boston, MAUnited States
Grammati SarriCytel Inc.LondonUnited Kingdom
Jamie ElvidgeNational Institute for Health and Care Excellence (NICE)ManchesterUnited Kingdom
Jeremy DietzNational Institute for Health and Care Excellence (NICE)LondonUnited Kingdom
Konstantina ChalkouInstitute of Social and Preventive Medicine (ISPM), University of BernBernSwitzerland
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7.2.3 Hierarchical metaregression

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We first investigate the event rate of patients receiving routine care:

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The forest plot above indicates that the baseline risk in the observational study from Morbach et al. is much higher than most trials.

We fitted an HMR model to the available RWD and published AD:

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           nr.adapt = 1000,         # Number of iteration for burnin 
            nr.thin = 1)             # Thinning rate
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We start our analysis by visualizing the conflict of evidence between the different types of data and study types. The figure below depicts the posterior distribution of \(\mu_{\phi}\), which is the mean bias of the IPD-NRS compared to the AD-RCTs control groups. The posterior distribution has a substantial probability mass on the right of zero, which indicates that in average the IPD-NRS patients present a better prognoses than the AD-RCTs control groups. That means that taking the IPD-NRS results at face value would be misleading if we aim to combine them with a meta-analysis of AD-RCTs.

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We start our analysis by visualizing the conflict of evidence between the different types of data and study types. The figure below depicts the posterior distribution of \(\mu_{\phi}\), which is the mean bias of the IPD-NRS compared to the AD-RCTs control groups. The posterior distribution has a substantial probability mass below zero, which indicates that in average the IPD-NRS patients present a better prognoses than the AD-RCTs control groups. That means that taking the IPD-NRS results at face value would be misleading if we aim to combine them with a meta-analysis of AD-RCTs.

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Conflict of evidence analysis. The left panel shows the prior to posterior sensitivity analysis of bias mean between the RCTs and the IPD-NRS. The right panel depicts the posterior distribution of the outliers detection weights.

The figure below presents the posterior distribution of the weights \(w_{i}\) for each study included in the HMR. These posteriors are summarized using a forest plot, where posterior intervals substantially greater than one indicate outliers. One important aspect of the HMR is that those outliers are automatically down-weighted in the analysis.

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Posterior distribution of the weights for each study included in the HMR
@@ -719,7 +726,7 @@

Version info

This chapter was rendered using the following version of R and its packages:

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R version 4.2.3 (2023-03-15 ucrt)
 Platform: x86_64-w64-mingw32/x64 (64-bit)
@@ -742,33 +749,34 @@ 
Version info

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Meta-analysis of clinical trials and real-world data", "section": "7.2 Hierarchical Meta-Regression", - "text": "7.2 Hierarchical Meta-Regression\nWe illustrate the implementation of hierarchical meta-regression using an example that involves the following data sources:\n\nAggregate data from 35 randomized trials investigating the efficacy of adjunctive treatments in managing diabetic foot problems compared with routine care\nIndividual participant data from a prospective cohort study investigating patient and limb survival in patients with diabetic foot ulcers\n\n\n7.2.1 Aggregate data\nWe first retrieve the randomized evidence and summarize the treatment effect estimates using a random effects meta-analysis:\n\nlibrary(dplyr)\nlibrary(jarbes)\nlibrary(meta)\n\ndata(\"healing\")\n\nresults.ADJ <- metabin(event.c = y_c, n.c = n_c,\n event.e = y_t, n.e = n_t,\n studlab = Study, data = healing,\n sm = \"OR\", \n prediction = TRUE)\n\nThe corresponding forest plot is depicted below. The endpoint is healing without amputations within a period less than or equal to 1 year.\n\n\n\n\n\nThe random effects meta-analysis yielded a pooled odds ratio of 1.90. However, substantial between-study heterogeneity was found, with \\(\\tau\\) = 0.46.\n\n\n7.2.2 Individual participant data\nSubsequently, we retrieve the individual participant data:\n\ndata(\"healingipd\")\nIPD <- healingipd %>% dplyr::select(healing.without.amp, PAD, neuropathy,\n first.ever.lesion, no.continuous.care, \n male, diab.typ2, insulin, HOCHD, \n HOS, CRF, dialysis, DNOAP, smoking.ever, \n diabdur, wagner.class)\n\nBriefly, these IPD were obtained from a prospective cohort study enrolling consecutive patients with diabetic foot ulcers (DFUs) and without previous major amputation in a single diabetes center between June 1998 and December 1999 (Morbach et al. 2012). The baseline characteristics of the study population are summarized below:\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nHealing without amputation\n(N=165)\nNo healing without amputation\n(N=95)\nOverall\n(N=260)\n\n\n\n\nAge (years)\n\n\n\n\n\nMean (SD)\n69.1 (10.9)\n68.5 (11.0)\n68.9 (10.9)\n\n\nMedian [Min, Max]\n70.0 [25.0, 90.0]\n69.0 [36.0, 91.0]\n70.0 [25.0, 91.0]\n\n\nDiabetes duration (years)\n\n\n\n\n\nMean (SD)\n15.9 (10.3)\n15.9 (11.2)\n15.9 (10.6)\n\n\nMedian [Min, Max]\n14.0 [1.00, 53.0]\n14.0 [0, 50.0]\n14.0 [0, 53.0]\n\n\nSex\n\n\n\n\n\nFemale\n71 (43.0%)\n35 (36.8%)\n106 (40.8%)\n\n\nMale\n94 (57.0%)\n60 (63.2%)\n154 (59.2%)\n\n\nEver smoker\n\n\n\n\n\nYes\n97 (58.8%)\n57 (60.0%)\n154 (59.2%)\n\n\nNo\n68 (41.2%)\n38 (40.0%)\n106 (40.8%)\n\n\nDiabetes type 2\n\n\n\n\n\nYes\n150 (90.9%)\n79 (83.2%)\n229 (88.1%)\n\n\nNo\n15 (9.1%)\n16 (16.8%)\n31 (11.9%)\n\n\nPeripheral arterial disease\n\n\n\n\n\nYes\n82 (49.7%)\n66 (69.5%)\n148 (56.9%)\n\n\nNo\n83 (50.3%)\n29 (30.5%)\n112 (43.1%)\n\n\nNeuropathy\n\n\n\n\n\nYes\n144 (87.3%)\n80 (84.2%)\n224 (86.2%)\n\n\nNo\n21 (12.7%)\n15 (15.8%)\n36 (13.8%)\n\n\nFirst ever lesion\n\n\n\n\n\nYes\n70 (42.4%)\n44 (46.3%)\n114 (43.8%)\n\n\nNo\n95 (57.6%)\n51 (53.7%)\n146 (56.2%)\n\n\nNo continuous care\n\n\n\n\n\nYes\n115 (69.7%)\n62 (65.3%)\n177 (68.1%)\n\n\nNo\n50 (30.3%)\n33 (34.7%)\n83 (31.9%)\n\n\nInsulin dependent\n\n\n\n\n\nYes\n109 (66.1%)\n65 (68.4%)\n174 (66.9%)\n\n\nNo\n56 (33.9%)\n30 (31.6%)\n86 (33.1%)\n\n\nHistory of coronary events (CHD)\n\n\n\n\n\nYes\n31 (18.8%)\n21 (22.1%)\n52 (20.0%)\n\n\nNo\n134 (81.2%)\n74 (77.9%)\n208 (80.0%)\n\n\nHistory of stroke\n\n\n\n\n\nYes\n36 (21.8%)\n19 (20.0%)\n55 (21.2%)\n\n\nNo\n129 (78.2%)\n76 (80.0%)\n205 (78.8%)\n\n\nCharcot foot syndrome\n\n\n\n\n\nYes\n28 (17.0%)\n24 (25.3%)\n52 (20.0%)\n\n\nNo\n137 (83.0%)\n71 (74.7%)\n208 (80.0%)\n\n\nDialysis\n\n\n\n\n\nYes\n3 (1.8%)\n6 (6.3%)\n9 (3.5%)\n\n\nNo\n162 (98.2%)\n89 (93.7%)\n251 (96.5%)\n\n\nDNOAP\n\n\n\n\n\nYes\n19 (11.5%)\n10 (10.5%)\n29 (11.2%)\n\n\nNo\n146 (88.5%)\n85 (89.5%)\n231 (88.8%)\n\n\nWagner score\n\n\n\n\n\n1-2\n115 (69.7%)\n27 (28.4%)\n142 (54.6%)\n\n\n3-4-5\n50 (30.3%)\n68 (71.6%)\n118 (45.4%)\n\n\n\n\n\n\n\nAs depicted above, IPD are available from 260 patients. Some of these patients have similar characteristics to those enrolled in the randomized trials. However, other patients have comorbidities, where one or more risk factors prevent them to participate in the RCTs due to ethical reasons. For example, 118 patients have severe ulcer lesions (Wagner score 3 to 5), and 77 patients suffer from severe ulcer lesions and peripheral arterial disease (PAD). The question is: Can we generalize the benefit of adjuvant therapies observed in the RCTs to the subgroups of patients encountered in clinical practice?\n\n\n7.2.3 Hierarchical metaregression\nWe fitted an HMR model to the available RWD and published AD:\n\nset.seed(2022)\n\nAD <- healing %>% dplyr::select(yc = y_c, nc = n_c, \n yt = y_t, nt = n_t, Study = Study)\n\nmx2 <- hmr(data = AD, # Published aggregate data\n two.by.two = FALSE, # \n dataIPD = IPD, # Data frame of the IPD \n re = \"sm\", # Random effects model: \"sm\" scale mixtures \n link = \"logit\", # Link function of the random effects\n sd.mu.1 = 1, # Scale parameter for the prior of mu.1\n sd.mu.2 = 1, # Scale parameter for the prior of mu.2 \n sd.mu.phi = 1, # Scale parameter for the prior of mu.phi \n sigma.1.upper = 5, # Upper bound of the prior of sigma.1 \n sigma.2.upper = 5, # Upper bound of the prior of sigma.2\n sigma.beta.upper = 5, # Upper bound of the prior of sigma.beta\n sd.Fisher.rho = 1.25, # Scale parameter for the prior of rho\n df.estimate = TRUE, # If TRUE the degrees of freedom are estimated\n df.lower = 3, # Lower bound of the df's prior\n df.upper = 10, # Upper bound of the df's prior\n nr.chains = 2, # Number of MCMC chains\n nr.iterations = 10000, # Total number of iterations\n nr.adapt = 1000, # Number of iteration for burnin \n nr.thin = 1) # Thinning rate\n\nWe start our analysis by visualizing the conflict of evidence between the different types of data and study types. The figure below depicts the posterior distribution of \\(\\mu_{\\phi}\\), which is the mean bias of the IPD-NRS compared to the AD-RCTs control groups. The posterior distribution has a substantial probability mass on the right of zero, which indicates that in average the IPD-NRS patients present a better prognoses than the AD-RCTs control groups. That means that taking the IPD-NRS results at face value would be misleading if we aim to combine them with a meta-analysis of AD-RCTs.\n\n\n\n\n\nConflict of evidence analysis. The left panel shows the prior to posterior sensitivity analysis of bias mean between the RCTs and the IPD-NRS. The right panel depicts the posterior distribution of the outliers detection weights.\n\n\n\n\nThe figure below presents the posterior distribution of the weights \\(w_{i}\\) for each study included in the HMR. These posteriors are summarized using a forest plot, where posterior intervals substantially greater than one indicate outliers. One important aspect of the HMR is that those outliers are automatically down-weighted in the analysis.\n\n\n\n\n\nPosterior distribution of the weights for each study included in the HMR" + "text": "7.2 Hierarchical Meta-Regression\nWe illustrate the implementation of hierarchical meta-regression using an example that involves the following data sources:\n\nAggregate data from 35 randomized trials investigating the efficacy of adjunctive treatments in managing diabetic foot problems compared with routine care\nIndividual participant data from a prospective cohort study investigating patient and limb survival in patients with diabetic foot ulcers\n\n\n7.2.1 Aggregate data\nWe first retrieve the randomized evidence and summarize the treatment effect estimates using a random effects meta-analysis:\n\nlibrary(dplyr)\nlibrary(jarbes)\nlibrary(meta)\n\ndata(\"healing\")\n\nresults.ADJ <- metabin(event.c = y_c, n.c = n_c,\n event.e = y_t, n.e = n_t,\n studlab = Study, data = healing,\n sm = \"OR\", \n prediction = TRUE)\n\nThe corresponding forest plot is depicted below. The endpoint is healing without amputations within a period less than or equal to 1 year.\n\n\n\n\n\nThe random effects meta-analysis yielded a pooled odds ratio of 1.90. However, substantial between-study heterogeneity was found, with \\(\\tau\\) = 0.46.\n\n\n7.2.2 Individual participant data\nSubsequently, we retrieve the individual participant data:\n\ndata(\"healingipd\")\nIPD <- healingipd %>% dplyr::select(healing.without.amp, PAD, neuropathy,\n first.ever.lesion, no.continuous.care, \n male, diab.typ2, insulin, HOCHD, \n HOS, CRF, dialysis, DNOAP, smoking.ever, \n diabdur, wagner.class)\n\nBriefly, these IPD were obtained from a prospective cohort study enrolling consecutive patients with diabetic foot ulcers (DFUs) and without previous major amputation in a single diabetes center between June 1998 and December 1999 (Morbach et al. 2012). The baseline characteristics of the study population are summarized below:\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nHealing without amputation\n(N=165)\nNo healing without amputation\n(N=95)\nOverall\n(N=260)\n\n\n\n\nAge (years)\n\n\n\n\n\nMean (SD)\n69.1 (10.9)\n68.5 (11.0)\n68.9 (10.9)\n\n\nMedian [Min, Max]\n70.0 [25.0, 90.0]\n69.0 [36.0, 91.0]\n70.0 [25.0, 91.0]\n\n\nDiabetes duration (years)\n\n\n\n\n\nMean (SD)\n15.9 (10.3)\n15.9 (11.2)\n15.9 (10.6)\n\n\nMedian [Min, Max]\n14.0 [1.00, 53.0]\n14.0 [0, 50.0]\n14.0 [0, 53.0]\n\n\nSex\n\n\n\n\n\nFemale\n71 (43.0%)\n35 (36.8%)\n106 (40.8%)\n\n\nMale\n94 (57.0%)\n60 (63.2%)\n154 (59.2%)\n\n\nEver smoker\n\n\n\n\n\nYes\n97 (58.8%)\n57 (60.0%)\n154 (59.2%)\n\n\nNo\n68 (41.2%)\n38 (40.0%)\n106 (40.8%)\n\n\nDiabetes type 2\n\n\n\n\n\nYes\n150 (90.9%)\n79 (83.2%)\n229 (88.1%)\n\n\nNo\n15 (9.1%)\n16 (16.8%)\n31 (11.9%)\n\n\nPeripheral arterial disease\n\n\n\n\n\nYes\n82 (49.7%)\n66 (69.5%)\n148 (56.9%)\n\n\nNo\n83 (50.3%)\n29 (30.5%)\n112 (43.1%)\n\n\nNeuropathy\n\n\n\n\n\nYes\n144 (87.3%)\n80 (84.2%)\n224 (86.2%)\n\n\nNo\n21 (12.7%)\n15 (15.8%)\n36 (13.8%)\n\n\nFirst ever lesion\n\n\n\n\n\nYes\n70 (42.4%)\n44 (46.3%)\n114 (43.8%)\n\n\nNo\n95 (57.6%)\n51 (53.7%)\n146 (56.2%)\n\n\nNo continuous care\n\n\n\n\n\nYes\n115 (69.7%)\n62 (65.3%)\n177 (68.1%)\n\n\nNo\n50 (30.3%)\n33 (34.7%)\n83 (31.9%)\n\n\nInsulin dependent\n\n\n\n\n\nYes\n109 (66.1%)\n65 (68.4%)\n174 (66.9%)\n\n\nNo\n56 (33.9%)\n30 (31.6%)\n86 (33.1%)\n\n\nHistory of coronary events (CHD)\n\n\n\n\n\nYes\n31 (18.8%)\n21 (22.1%)\n52 (20.0%)\n\n\nNo\n134 (81.2%)\n74 (77.9%)\n208 (80.0%)\n\n\nHistory of stroke\n\n\n\n\n\nYes\n36 (21.8%)\n19 (20.0%)\n55 (21.2%)\n\n\nNo\n129 (78.2%)\n76 (80.0%)\n205 (78.8%)\n\n\nCharcot foot syndrome\n\n\n\n\n\nYes\n28 (17.0%)\n24 (25.3%)\n52 (20.0%)\n\n\nNo\n137 (83.0%)\n71 (74.7%)\n208 (80.0%)\n\n\nDialysis\n\n\n\n\n\nYes\n3 (1.8%)\n6 (6.3%)\n9 (3.5%)\n\n\nNo\n162 (98.2%)\n89 (93.7%)\n251 (96.5%)\n\n\nDNOAP\n\n\n\n\n\nYes\n19 (11.5%)\n10 (10.5%)\n29 (11.2%)\n\n\nNo\n146 (88.5%)\n85 (89.5%)\n231 (88.8%)\n\n\nWagner score\n\n\n\n\n\n1-2\n115 (69.7%)\n27 (28.4%)\n142 (54.6%)\n\n\n3-4-5\n50 (30.3%)\n68 (71.6%)\n118 (45.4%)\n\n\n\n\n\n\n\nAs depicted above, IPD are available from 260 patients. Some of these patients have similar characteristics to those enrolled in the randomized trials. However, other patients have comorbidities, where one or more risk factors prevent them to participate in the RCTs due to ethical reasons. For example, 118 patients have severe ulcer lesions (Wagner score 3 to 5), and 77 patients suffer from severe ulcer lesions and peripheral arterial disease (PAD). The question is: Can we generalize the benefit of adjuvant therapies observed in the RCTs to the subgroups of patients encountered in clinical practice?\n\n\n7.2.3 Hierarchical metaregression\nWe first investigate the event rate of patients receiving routine care:\n\n\n\n\n\nThe forest plot above indicates that the baseline risk in the observational study from Morbach et al. is much higher than most trials.\nWe fitted an HMR model to the available RWD and published AD:\n\nset.seed(2022)\n\nAD <- healing %>% dplyr::select(yc = y_c, nc = n_c, \n yt = y_t, nt = n_t, Study = Study)\n\nmx2 <- hmr(data = AD, # Published aggregate data\n two.by.two = FALSE, # \n dataIPD = IPD, # Data frame of the IPD \n re = \"sm\", # Random effects model: \"sm\" scale mixtures \n link = \"logit\", # Link function of the random effects\n sd.mu.1 = 1, # Scale parameter for the prior of mu.1\n sd.mu.2 = 1, # Scale parameter for the prior of mu.2 \n sd.mu.phi = 1, # Scale parameter for the prior of mu.phi \n sigma.1.upper = 5, # Upper bound of the prior of sigma.1 \n sigma.2.upper = 5, # Upper bound of the prior of sigma.2\n sigma.beta.upper = 5, # Upper bound of the prior of sigma.beta\n sd.Fisher.rho = 1.25, # Scale parameter for the prior of rho\n df.estimate = TRUE, # If TRUE the degrees of freedom are estimated\n df.lower = 3, # Lower bound of the df's prior\n df.upper = 10, # Upper bound of the df's prior\n nr.chains = 2, # Number of MCMC chains\n nr.iterations = 10000, # Total number of iterations\n nr.adapt = 1000, # Number of iteration for burnin \n nr.thin = 1) # Thinning rate\n\nWe start our analysis by visualizing the conflict of evidence between the different types of data and study types. The figure below depicts the posterior distribution of \\(\\mu_{\\phi}\\), which is the mean bias of the IPD-NRS compared to the AD-RCTs control groups. The posterior distribution has a substantial probability mass below zero, which indicates that in average the IPD-NRS patients present a better prognoses than the AD-RCTs control groups. That means that taking the IPD-NRS results at face value would be misleading if we aim to combine them with a meta-analysis of AD-RCTs.\n\n\n\n\n\nConflict of evidence analysis. The left panel shows the prior to posterior sensitivity analysis of bias mean between the RCTs and the IPD-NRS. The right panel depicts the posterior distribution of the outliers detection weights.\n\n\n\n\nThe figure below presents the posterior distribution of the weights \\(w_{i}\\) for each study included in the HMR. These posteriors are summarized using a forest plot, where posterior intervals substantially greater than one indicate outliers. One important aspect of the HMR is that those outliers are automatically down-weighted in the analysis.\n\n\n\n\n\nPosterior distribution of the weights for each study included in the HMR" }, { "objectID": "chapter_12.html#introduction", @@ -389,7 +389,7 @@ "href": "chapter_11.html#version-info", "title": "7  Individual Participant Data Meta-analysis of clinical trials and real-world data", "section": "Version info", - "text": "Version info\nThis chapter was rendered using the following version of R and its packages:\n\n\nR version 4.2.3 (2023-03-15 ucrt)\nPlatform: x86_64-w64-mingw32/x64 (64-bit)\nRunning under: Windows 10 x64 (build 19045)\n\nMatrix products: default\n\nlocale:\n[1] LC_COLLATE=Dutch_Netherlands.utf8 LC_CTYPE=Dutch_Netherlands.utf8 \n[3] LC_MONETARY=Dutch_Netherlands.utf8 LC_NUMERIC=C \n[5] LC_TIME=Dutch_Netherlands.utf8 \n\nattached base packages:\n[1] stats graphics grDevices utils datasets methods base \n\nother attached packages:\n [1] meta_6.5-0 table1_1.4.3 tableone_0.13.2 dplyr_1.1.2 \n [5] jarbes_2.0.0 GGally_2.1.2 R2jags_0.7-1 rjags_4-14 \n [9] mcmcplots_0.4.3 coda_0.19-4 gridExtra_2.3 ggplot2_3.4.4 \n[13] kableExtra_1.3.4\n\nloaded via a namespace (and not attached):\n [1] httr_1.4.7 tidyr_1.3.0 sfsmisc_1.1-16 \n [4] jsonlite_1.8.7 viridisLite_0.4.2 splines_4.2.3 \n [7] Formula_1.2-5 shiny_1.8.0 metafor_4.4-0 \n[10] yaml_2.3.7 numDeriv_2016.8-1.1 R2WinBUGS_2.1-21 \n[13] pillar_1.9.0 lattice_0.21-8 glue_1.6.2 \n[16] digest_0.6.31 RColorBrewer_1.1-3 promises_1.2.1 \n[19] minqa_1.2.6 rvest_1.0.3 colorspace_2.1-0 \n[22] htmltools_0.5.5 httpuv_1.6.12 Matrix_1.5-4.1 \n[25] survey_4.2-1 plyr_1.8.8 pkgconfig_2.0.3 \n[28] purrr_1.0.1 xtable_1.8-4 scales_1.2.1 \n[31] webshot_0.5.5 svglite_2.1.1 later_1.3.1 \n[34] metadat_1.2-0 lme4_1.1-35.1 tibble_3.2.1 \n[37] generics_0.1.3 ellipsis_0.3.2 withr_2.5.2 \n[40] cli_3.6.1 survival_3.5-5 magrittr_2.0.3 \n[43] mime_0.12 evaluate_0.23 fansi_1.0.4 \n[46] nlme_3.1-162 MASS_7.3-60 xml2_1.3.4 \n[49] tools_4.2.3 mitools_2.4 lifecycle_1.0.4 \n[52] stringr_1.5.1 munsell_0.5.0 compiler_4.2.3 \n[55] systemfonts_1.0.4 rlang_1.1.1 nloptr_2.0.3 \n[58] grid_4.2.3 rstudioapi_0.15.0 CompQuadForm_1.4.3 \n[61] htmlwidgets_1.6.2 miniUI_0.1.1.1 rmarkdown_2.25 \n[64] boot_1.3-28.1 gtable_0.3.4 abind_1.4-5 \n[67] DBI_1.1.3 reshape_0.8.9 R6_2.5.1 \n[70] knitr_1.45 denstrip_1.5.4 fastmap_1.1.1 \n[73] utf8_1.2.3 mathjaxr_1.6-0 ggExtra_0.10.1 \n[76] stringi_1.7.12 parallel_4.2.3 Rcpp_1.0.10 \n[79] vctrs_0.6.3 tidyselect_1.2.0 xfun_0.39" + "text": "Version info\nThis chapter was rendered using the following version of R and its packages:\n\n\nR version 4.2.3 (2023-03-15 ucrt)\nPlatform: x86_64-w64-mingw32/x64 (64-bit)\nRunning under: Windows 10 x64 (build 19045)\n\nMatrix products: default\n\nlocale:\n[1] LC_COLLATE=Dutch_Netherlands.utf8 LC_CTYPE=Dutch_Netherlands.utf8 \n[3] LC_MONETARY=Dutch_Netherlands.utf8 LC_NUMERIC=C \n[5] LC_TIME=Dutch_Netherlands.utf8 \n\nattached base packages:\n[1] stats graphics grDevices utils datasets methods base \n\nother attached packages:\n [1] meta_6.5-0 table1_1.4.3 tableone_0.13.2 dplyr_1.1.2 \n [5] jarbes_2.0.0 GGally_2.1.2 R2jags_0.7-1 rjags_4-14 \n [9] mcmcplots_0.4.3 coda_0.19-4 gridExtra_2.3 ggplot2_3.4.4 \n[13] kableExtra_1.3.4\n\nloaded via a namespace (and not attached):\n [1] nlme_3.1-162 webshot_0.5.5 RColorBrewer_1.1-3 \n [4] httr_1.4.7 numDeriv_2016.8-1.1 tools_4.2.3 \n [7] utf8_1.2.3 R6_2.5.1 R2WinBUGS_2.1-21 \n[10] metafor_4.4-0 DBI_1.1.3 colorspace_2.1-0 \n[13] withr_2.5.2 tidyselect_1.2.0 compiler_4.2.3 \n[16] cli_3.6.1 rvest_1.0.3 denstrip_1.5.4 \n[19] xml2_1.3.4 labeling_0.4.3 scales_1.2.1 \n[22] sfsmisc_1.1-16 systemfonts_1.0.4 stringr_1.5.1 \n[25] digest_0.6.31 minqa_1.2.6 rmarkdown_2.25 \n[28] svglite_2.1.1 pkgconfig_2.0.3 htmltools_0.5.5 \n[31] lme4_1.1-35.1 fastmap_1.1.1 htmlwidgets_1.6.2 \n[34] rlang_1.1.1 rstudioapi_0.15.0 shiny_1.8.0 \n[37] farver_2.1.1 generics_0.1.3 jsonlite_1.8.7 \n[40] magrittr_2.0.3 Formula_1.2-5 metadat_1.2-0 \n[43] Matrix_1.5-4.1 Rcpp_1.0.10 munsell_0.5.0 \n[46] fansi_1.0.4 abind_1.4-5 lifecycle_1.0.4 \n[49] stringi_1.7.12 yaml_2.3.7 CompQuadForm_1.4.3 \n[52] mathjaxr_1.6-0 MASS_7.3-60 plyr_1.8.8 \n[55] grid_4.2.3 parallel_4.2.3 promises_1.2.1 \n[58] miniUI_0.1.1.1 lattice_0.21-8 splines_4.2.3 \n[61] knitr_1.45 pillar_1.9.0 boot_1.3-28.1 \n[64] codetools_0.2-19 glue_1.6.2 evaluate_0.23 \n[67] mitools_2.4 vctrs_0.6.3 nloptr_2.0.3 \n[70] httpuv_1.6.12 gtable_0.3.4 purrr_1.0.1 \n[73] tidyr_1.3.0 reshape_0.8.9 xfun_0.39 \n[76] ggExtra_0.10.1 mime_0.12 xtable_1.8-4 \n[79] survey_4.2-1 later_1.3.1 survival_3.5-5 \n[82] viridisLite_0.4.2 tibble_3.2.1 ellipsis_0.3.2" }, { "objectID": "chapter_11.html#references", @@ -397,5 +397,19 @@ "title": "7  Individual Participant Data Meta-analysis of clinical trials and real-world data", "section": "References", "text": "References\n\n\n\n\nMorbach, Stephan, Heike Furchert, Ute Gröblinghoff, Heribert Hoffmeier, Kerstin Kersten, Gerd-Thomas Klauke, Ulrike Klemp, et al. 2012. “Long-Term Prognosis of Diabetic Foot Patients and Their Limbs.” Diabetes Care 35 (10): 2021–27. https://doi.org/10.2337/dc12-0200." + }, + { + "objectID": "authors.html#about-this-book", + "href": "authors.html#about-this-book", + "title": "11  Book Authors", + "section": "11.1 About this book", + "text": "11.1 About this book\nWe gratefully acknowledge the contribution from the following authors:\n\n\n\nAuthor\n\n\n\n\n\n\nAlf Scotland\nBiogen Digital Health International GmbH\nBaar, Switzerland\n\n\nAmr Makady\nThe Janssen Pharmaceutical Companies of Johnson & Johnson\nBreda, The Netherlands\n\n\nChristina Read\nUtrecht University\nUtrecht, The Netherlands\n\n\nGrammati Sarri\nCytel, Inc.\nLondon, United Kingdom\n\n\nJamie Elvidge\nNational Institute for Health and Care Excellence\nManchester, United Kingdom" + }, + { + "objectID": "authors.html", + "href": "authors.html", + "title": "11  Book Authors", + "section": "", + "text": "We gratefully acknowledge the contribution from the following authors:\n\nAuthors\n\n\n\n\n\n\n\n\nAuthor\nAffiliation\nCity\nCountry\n\n\n\n\nAlf Scotland\nBiogen Digital Health International GmbH\nBaar\nSwitzerland\n\n\nAmr Makady\nThe Janssen Pharmaceutical Companies of Johnson & Johnson\nBreda\nThe Netherlands\n\n\nChristina Read\nUtrecht University\nUtrecht\nThe Netherlands\n\n\nElvira D’Andrea\nAbbVie Inc.\nBoston, MA\nUnited States\n\n\nGrammati Sarri\nCytel Inc.\nLondon\nUnited Kingdom\n\n\nJamie Elvidge\nNational Institute for Health and Care Excellence (NICE)\nManchester\nUnited Kingdom\n\n\nJeremy Dietz\nNational Institute for Health and Care Excellence (NICE)\nLondon\nUnited Kingdom\n\n\nKonstantina Chalkou\nInstitute of Social and Preventive Medicine (ISPM), University of Bern\nBern\nSwitzerland" } ] \ No newline at end of file diff --git a/resources/chapter 12/fig_functions.r b/resources/chapter 12/fig_functions.r index 947524d..f38a391 100644 --- a/resources/chapter 12/fig_functions.r +++ b/resources/chapter 12/fig_functions.r @@ -22,8 +22,6 @@ plot_distribution_progression <- function(sim_data) { } ggplot_distribution_edss <- function(sim_data) { - require(ggplot2) - sim_data$Treatment <- factor(sim_data$x, levels = c(0,1), labels = c("DMT A", "DMT B")) sim_data$time <- as.factor(sim_data$time) diff --git a/resources/orcid.png b/resources/orcid.png new file mode 100644 index 0000000000000000000000000000000000000000..a48ffa0f96c9b9b182af22ec3fd3f6807abcb4fd GIT binary patch literal 38257 zcmXs#2Rv2n|HsPS$_h8GtZYKoE%OqE>{Z!hC!3o}lzFX;kX3fbo=Ie{PdLwhLKZ4l=p22^!yQy^( zg36-FPaY6~zay;Fb+sVK?#@D92eauE0|89A&Cl7wmO!VKle!oTKdZIPKlO@8TzMJlN z+9W*kSLUC`Y0T}U?P%O0xpE~@xs5yW68`6_Kd`AtR+0OUCjE6v_4ew5e_Uy=`4Ewl z{Bx#%JMAT_C0B3I{*Xwsq0@#lAm_-;_fV^;s;u7QnL@D3cV$nT9!O2QL?BhjNtdm| z)>NQ5wztv)zx~JUG5+3I-QswgTuf|d!XuPwGEcCxl}%B+e~N1=AD>%S#tUxAj3dU# zd#;gdtY-rS#$pcyOkHz6#-^C%Pl-BKj7qT9J`m6oPX1Wwh%H->x?LzxZ817}SXA1? 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