From f554a75c82832c2f4d3d2084be77efa09af84cde Mon Sep 17 00:00:00 2001 From: Thomas Debray <117118104+tdebray123@users.noreply.github.com> Date: Fri, 24 Nov 2023 14:27:18 +0100 Subject: [PATCH] expand chapter Pablo --- _freeze/chapter_11/execute-results/html.json | 4 +- .../figure-html/unnamed-chunk-7-1.png | Bin 66072 -> 68114 bytes _quarto.yml | 1 + authors.qmd | 23 ++++ chapter_11.qmd | 56 ++++++-- .../figure-html/unnamed-chunk-7-1.png | Bin 66072 -> 68114 bytes docs/chapter_11.html | 120 +++++++++++++++++- .../figure-html/unnamed-chunk-7-1.png | Bin 66072 -> 68114 bytes docs/chapter_12.html | 6 + docs/search.json | 2 +- 10 files changed, 194 insertions(+), 18 deletions(-) create mode 100644 authors.qmd diff --git a/_freeze/chapter_11/execute-results/html.json b/_freeze/chapter_11/execute-results/html.json index 7fff046..9f1f44b 100644 --- a/_freeze/chapter_11/execute-results/html.json +++ b/_freeze/chapter_11/execute-results/html.json @@ -1,7 +1,7 @@ { - "hash": "3aa5604f0f0c029129672ddf63b3032a", + "hash": "12fb8d715d1d2ead348f6ec7153a3975", "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 is summarized below:\n\n\n::: {.cell hash='chapter_11_cache/html/unnamed-chunk-5_61ed19f8aa810f3baaa397ee20e963d7'}\n::: {.cell-output-display}\n```{=html}\n
\n | Overall (N=260) | \n
---|---|
Age (years) | \n\n |
Mean (SD) | \n68.9 (10.9) | \n
Median [Min, Max] | \n70.0 [25.0, 91.0] | \n
Diabetes duration (years) | \n\n |
Mean (SD) | \n15.9 (10.6) | \n
Median [Min, Max] | \n14.0 [0, 53.0] | \n
Sex | \n\n |
Female | \n106 (40.8%) | \n
Male | \n154 (59.2%) | \n
Ever smoker | \n\n |
Yes | \n154 (59.2%) | \n
No | \n106 (40.8%) | \n
Diabetes type 2 | \n\n |
Yes | \n229 (88.1%) | \n
No | \n31 (11.9%) | \n
Peripheral arterial disease | \n\n |
Yes | \n148 (56.9%) | \n
No | \n112 (43.1%) | \n
Neuropathy | \n\n |
Yes | \n224 (86.2%) | \n
No | \n36 (13.8%) | \n
First ever lesion | \n\n |
Yes | \n114 (43.8%) | \n
No | \n146 (56.2%) | \n
No continuous care | \n\n |
Yes | \n177 (68.1%) | \n
No | \n83 (31.9%) | \n
Insulin dependent | \n\n |
Yes | \n174 (66.9%) | \n
No | \n86 (33.1%) | \n
History of coronary events (CHD) | \n\n |
Yes | \n52 (20.0%) | \n
No | \n208 (80.0%) | \n
History of stroke | \n\n |
Yes | \n55 (21.2%) | \n
No | \n205 (78.8%) | \n
Charcot foot syndrome | \n\n |
Yes | \n52 (20.0%) | \n
No | \n208 (80.0%) | \n
Dialysis | \n\n |
Yes | \n9 (3.5%) | \n
No | \n251 (96.5%) | \n
DNOAP | \n\n |
Yes | \n29 (11.2%) | \n
No | \n231 (88.8%) | \n
Wagner score | \n\n |
1-2 | \n142 (54.6%) | \n
3-4-5 | \n118 (45.4%) | \n
\n | Healing without amputation (N=165) | \nNo healing without amputation (N=95) | \nOverall (N=260) | \n
---|---|---|---|
Age (years) | \n\n | \n | \n |
Mean (SD) | \n69.1 (10.9) | \n68.5 (11.0) | \n68.9 (10.9) | \n
Median [Min, Max] | \n70.0 [25.0, 90.0] | \n69.0 [36.0, 91.0] | \n70.0 [25.0, 91.0] | \n
Diabetes duration (years) | \n\n | \n | \n |
Mean (SD) | \n15.9 (10.3) | \n15.9 (11.2) | \n15.9 (10.6) | \n
Median [Min, Max] | \n14.0 [1.00, 53.0] | \n14.0 [0, 50.0] | \n14.0 [0, 53.0] | \n
Sex | \n\n | \n | \n |
Female | \n71 (43.0%) | \n35 (36.8%) | \n106 (40.8%) | \n
Male | \n94 (57.0%) | \n60 (63.2%) | \n154 (59.2%) | \n
Ever smoker | \n\n | \n | \n |
Yes | \n97 (58.8%) | \n57 (60.0%) | \n154 (59.2%) | \n
No | \n68 (41.2%) | \n38 (40.0%) | \n106 (40.8%) | \n
Diabetes type 2 | \n\n | \n | \n |
Yes | \n150 (90.9%) | \n79 (83.2%) | \n229 (88.1%) | \n
No | \n15 (9.1%) | \n16 (16.8%) | \n31 (11.9%) | \n
Peripheral arterial disease | \n\n | \n | \n |
Yes | \n82 (49.7%) | \n66 (69.5%) | \n148 (56.9%) | \n
No | \n83 (50.3%) | \n29 (30.5%) | \n112 (43.1%) | \n
Neuropathy | \n\n | \n | \n |
Yes | \n144 (87.3%) | \n80 (84.2%) | \n224 (86.2%) | \n
No | \n21 (12.7%) | \n15 (15.8%) | \n36 (13.8%) | \n
First ever lesion | \n\n | \n | \n |
Yes | \n70 (42.4%) | \n44 (46.3%) | \n114 (43.8%) | \n
No | \n95 (57.6%) | \n51 (53.7%) | \n146 (56.2%) | \n
No continuous care | \n\n | \n | \n |
Yes | \n115 (69.7%) | \n62 (65.3%) | \n177 (68.1%) | \n
No | \n50 (30.3%) | \n33 (34.7%) | \n83 (31.9%) | \n
Insulin dependent | \n\n | \n | \n |
Yes | \n109 (66.1%) | \n65 (68.4%) | \n174 (66.9%) | \n
No | \n56 (33.9%) | \n30 (31.6%) | \n86 (33.1%) | \n
History of coronary events (CHD) | \n\n | \n | \n |
Yes | \n31 (18.8%) | \n21 (22.1%) | \n52 (20.0%) | \n
No | \n134 (81.2%) | \n74 (77.9%) | \n208 (80.0%) | \n
History of stroke | \n\n | \n | \n |
Yes | \n36 (21.8%) | \n19 (20.0%) | \n55 (21.2%) | \n
No | \n129 (78.2%) | \n76 (80.0%) | \n205 (78.8%) | \n
Charcot foot syndrome | \n\n | \n | \n |
Yes | \n28 (17.0%) | \n24 (25.3%) | \n52 (20.0%) | \n
No | \n137 (83.0%) | \n71 (74.7%) | \n208 (80.0%) | \n
Dialysis | \n\n | \n | \n |
Yes | \n3 (1.8%) | \n6 (6.3%) | \n9 (3.5%) | \n
No | \n162 (98.2%) | \n89 (93.7%) | \n251 (96.5%) | \n
DNOAP | \n\n | \n | \n |
Yes | \n19 (11.5%) | \n10 (10.5%) | \n29 (11.2%) | \n
No | \n146 (88.5%) | \n85 (89.5%) | \n231 (88.8%) | \n
Wagner score | \n\n | \n | \n |
1-2 | \n115 (69.7%) | \n27 (28.4%) | \n142 (54.6%) | \n
3-4-5 | \n50 (30.3%) | \n68 (71.6%) | \n118 (45.4%) | \n
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