We use a Bayesian approach to run individual patient data meta-analysis and network meta-analysis using 'JAGS'. The methods incorporate shrinkage methods and calculate patient-specific treatment effects as described in Seo et al. (2021) <doi:10.1002/sim.8859>. This package also includes user-friendly functions that impute missing data in an individual patient data using mice-related packages.
Version: | 0.3 |
Depends: | R (≥ 2.10) |
Imports: | rjags (≥ 4-6), coda (≥ 0.13), mvtnorm, dplyr |
Suggests: | dclone, R2WinBUGS, mice, micemd, miceadds, mitools, knitr, rmarkdown |
Published: | 2022-06-05 |
Author: | Michael Seo [aut, cre] |
Maintainer: | Michael Seo <swj8874 at gmail.com> |
License: | GPL-3 |
NeedsCompilation: | no |
Citation: | bipd citation info |
Materials: | NEWS |
In views: | MetaAnalysis |
CRAN checks: | bipd results |
Reference manual: | bipd.pdf |
Vignettes: |
IPD meta-analysis-with-missing-data IPD meta-analysis Imputing missing values in IPD |
Package source: | bipd_0.3.tar.gz |
Windows binaries: | r-devel: bipd_0.3.zip, r-release: bipd_0.3.zip, r-oldrel: bipd_0.3.zip |
macOS binaries: | r-release (arm64): bipd_0.3.tgz, r-oldrel (arm64): bipd_0.3.tgz, r-release (x86_64): bipd_0.3.tgz, r-oldrel (x86_64): bipd_0.3.tgz |
Old sources: | bipd archive |
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