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:Yueqi Shen [aut, cre], Matthew A. Psioda [aut], Joseph G. Ibrahim [aut]

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'))

Peer review:

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
  • openmp– GCC OpenMP (GOMP) support library
Datasets:
  • 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.

openblascppopenmp

1.48 score 230 downloads 5 exports 24 dependencies

Last updated 8 months agofrom:f8f06a5dde. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKDec 06 2024
R-4.5-linux-x86_64OKDec 06 2024

Exports:approximate.prior.betaphm.fixed.a0phm.random.a0power.phm.fixed.a0power.phm.random.a0

Dependencies:clicpp11dplyrfansigenericsgluelifecyclemagrittrpillarpkgconfigpurrrR6RcppRcppArmadilloRcppDistrlangstringistringrtibbletidyrtidyselectutf8vctrswithr