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BayesPET: Bayesian Prediction of Event Times for Blinded Randomized Controlled Trials

Bayesian methods for predicting the calendar time at which a target number of events is reached in clinical trials. The methodology applies to both blinded and unblinded settings and jointly models enrollment, event-time, and censoring processes. The package provides tools for trial data simulation, model fitting using 'Stan' via the 'rstan' interface, and event time prediction under a wide range of trial designs, including varying sample sizes, enrollment patterns, treatment effects, and event or censoring time distributions. The package is intended to support interim monitoring, operational planning, and decision-making in clinical trial development. Methods are described in Fu et al. (2025) <doi:10.1002/sim.70310>.

Version: 0.1.0
Depends: R (≥ 4.1.0)
Imports: dplyr, magrittr, methods, Rcpp (≥ 0.12.0), RcppParallel (≥ 5.0.1), rstan (≥ 2.18.1), utils, furrr, future, readr, tibble, tidyr, reshape2
LinkingTo: BH (≥ 1.66.0), Rcpp (≥ 0.12.0), RcppEigen (≥ 0.3.3.3.0), RcppParallel (≥ 5.0.1), rstan (≥ 2.18.1), StanHeaders (≥ 2.18.0)
Published: 2026-02-12
DOI: 10.32614/CRAN.package.BayesPET
Author: Xinyi He [cre, aut], Jingyan Fu [aut], Ying Yuan [aut, cph]
Maintainer: Xinyi He <xinyi.he at uth.tmc.edu>
License: GPL (≥ 3)
NeedsCompilation: yes
SystemRequirements: GNU make
Materials: NEWS
CRAN checks: BayesPET results

Documentation:

Reference manual: BayesPET.html , BayesPET.pdf

Downloads:

Package source: BayesPET_0.1.0.tar.gz
Windows binaries: r-devel: BayesPET_0.1.0.zip, r-release: BayesPET_0.1.0.zip, r-oldrel: BayesPET_0.1.0.zip
macOS binaries: r-release (arm64): BayesPET_0.1.0.tgz, r-oldrel (arm64): BayesPET_0.1.0.tgz, r-release (x86_64): BayesPET_0.1.0.tgz, r-oldrel (x86_64): BayesPET_0.1.0.tgz

Linking:

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

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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