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 Documentation: Downloads: Linking:Please use the canonical form https://CRAN.R-project.org/package=bonsaiforest to link to this page.
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