Package: BayesPPDSurv 1.0.3
Yueqi Shen
BayesPPDSurv: Bayesian Power Prior Design for Survival Data
Bayesian power/type I error calculation and model fitting using the power prior and the normalized power prior for proportional hazards models with piecewise constant hazard. The methodology and examples of applying the package are detailed in <doi:10.48550/arXiv.2404.05118>. 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 proportional hazards model with piecewise constant hazard is detailed in Ibrahim et al. (2001) <doi:10.1007/978-1-4757-3447-8>.
Authors:
BayesPPDSurv_1.0.3.tar.gz
BayesPPDSurv_1.0.3.tar.gz(r-4.5-noble)BayesPPDSurv_1.0.3.tar.gz(r-4.4-noble)
BayesPPDSurv_1.0.3.tgz(r-4.4-emscripten)BayesPPDSurv_1.0.3.tgz(r-4.3-emscripten)
BayesPPDSurv.pdf |BayesPPDSurv.html✨
BayesPPDSurv/json (API)
NEWS
| # Install 'BayesPPDSurv' in R: |
| install.packages('BayesPPDSurv', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org')) |
- melanoma - Melanoma Clinical Trials E1684 and E1690
This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.
Last updated 8 months agofrom:f8f06a5dde. Checks:OK: 2. Indexed: yes.
| Target | Result | Date |
|---|---|---|
| Doc / Vignettes | OK | Dec 06 2024 |
| R-4.5-linux-x86_64 | OK | Dec 06 2024 |
Exports:approximate.prior.betaphm.fixed.a0phm.random.a0power.phm.fixed.a0power.phm.random.a0
Dependencies:clicpp11dplyrfansigenericsgluelifecyclemagrittrpillarpkgconfigpurrrR6RcppRcppArmadilloRcppDistrlangstringistringrtibbletidyrtidyselectutf8vctrswithr
