Subgroup analyses are routinely performed in clinical trial
analyses. From a methodological perspective, two key issues of
subgroup analyses are multiplicity (even if only predefined subgroups
are investigated) and the low sample sizes of subgroups which lead to
highly variable estimates, see e.g. Yusuf et al (1991)
<doi:10.1001/jama.1991.03470010097038>. This package implements
subgroup estimates based on Bayesian shrinkage priors, see Carvalho et
al (2019) <https://proceedings.mlr.press/v5/carvalho09a.html>. In
addition, estimates based on penalized likelihood inference are
available, based on Simon et al (2011) <doi:10.18637/jss.v039.i05>.
The corresponding shrinkage based forest plots address the
aforementioned issues and can complement standard forest plots in
practical clinical trial analyses.
| Version: |
0.1.1 |
| Depends: |
R (≥ 4.1) |
| Imports: |
brms (≥ 2.22.0), broom, checkmate, dplyr, forcats, gbm, ggplot2, glmnet, MASS, Rcpp, splines2, stats, survival, tibble, tidyr, tidyselect, vdiffr |
| LinkingTo: |
Rcpp, RcppArmadillo |
| Suggests: |
knitr, rmarkdown, testthat (≥ 3.0.0) |
| Published: |
2024-09-27 |
| DOI: |
10.32614/CRAN.package.bonsaiforest |
| Author: |
Mar Vazquez Rabunal [aut],
Daniel Sabanés Bové [aut],
Marcel Wolbers [aut],
Isaac Gravestock [cre],
F. Hoffmann-La Roche AG [cph, fnd] |
| Maintainer: |
Isaac Gravestock <isaac.gravestock at roche.com> |
| BugReports: |
https://github.com/insightsengineering/bonsaiforest/issues |
| License: |
Apache License 2.0 |
| URL: |
https://github.com/insightsengineering/bonsaiforest/ |
| NeedsCompilation: |
yes |
| Language: |
en-US |
| Materials: |
NEWS |
| CRAN checks: |
bonsaiforest results |