bayesCT: Simulation and Analysis of Adaptive Bayesian Clinical Trials

Simulation and analysis of Bayesian adaptive clinical trials for binomial, Gaussian, and time-to-event data types, incorporates historical data and allows early stopping for futility or early success. The package uses novel and efficient Monte Carlo methods for estimating Bayesian posterior probabilities, evaluation of loss to follow up, and imputation of incomplete data. The package has the functionality for dynamically incorporating historical data into the analysis via the power prior or non-informative priors.

Version: 0.99.3
Depends: R (≥ 2.10)
Imports: bayesDP, dplyr, purrr, survival, magrittr (≥ 1.5)
Suggests: testthat, rmarkdown, pkgdown, devtools, knitr
Published: 2020-07-01
Author: Thevaa Chandereng ORCID iD [aut, cre, cph], Donald Musgrove [aut, cph], Tarek Haddad [aut, cph], Graeme Hickey [aut, cph], Timothy Hanson [aut, cph], Theodore Lystig [aut, cph]
Maintainer: Thevaa Chandereng <chandereng at wisc.edu>
BugReports: https://github.com/thevaachandereng/bayesCT/issues/
License: GPL-3
URL: https://github.com/thevaachandereng/bayesCT/
NeedsCompilation: no
Materials: README
CRAN checks: bayesCT results

Downloads:

Reference manual: bayesCT.pdf
Vignettes: bayesian trial
bayesCT:binomial
bayesCT:normal
bayesCT:survival
Package source: bayesCT_0.99.3.tar.gz
Windows binaries: r-devel: bayesCT_0.99.3.zip, r-release: bayesCT_0.99.3.zip, r-oldrel: not available
macOS binaries: r-release: bayesCT_0.99.2.tgz, r-oldrel: bayesCT_0.99.3.tgz
Old sources: bayesCT archive

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