Bayesian power/type I error calculation and model fitting using the power prior and the normalized power prior for generalized linear models. Detailed examples of applying the package are available at <doi:10.32614/RJ-2023-016>. Models for time-to-event outcomes are implemented in the R package 'BayesPPDSurv'. The Bayesian clinical trial design methodology is described in Chen et al. (2011) <doi:10.1111/j.1541-0420.2011.01561.x>, and Psioda and Ibrahim (2019) <doi:10.1093/biostatistics/kxy009>. The normalized power prior is described in Duan et al. (2006) <doi:10.1002/env.752> and Ibrahim et al. (2015) <doi:10.1002/sim.6728>.
Version: 1.1.3 Depends: R (≥ 3.5.0) Imports: Rcpp LinkingTo: Rcpp, RcppArmadillo, RcppEigen, RcppNumerical Suggests: rmarkdown, knitr, testthat (≥ 3.0.0), ggplot2, kableExtra Published: 2025-01-13 DOI: 10.32614/CRAN.package.BayesPPD Author: Yueqi Shen [aut, cre], Matthew A. Psioda [aut], Joseph G. Ibrahim [aut] Maintainer: Yueqi Shen <angieshen6 at gmail.com> License: GPL (≥ 3) NeedsCompilation: yes Citation: BayesPPD citation info Materials: NEWS CRAN checks: BayesPPD results Documentation: Downloads: Reverse dependencies: Linking:Please use the canonical form https://CRAN.R-project.org/package=BayesPPD to link to this page.
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