Package: BACCT 1.0

Hongtao Zhang

BACCT: Bayesian Augmented Control for Clinical Trials

Implements the Bayesian Augmented Control (BAC, a.k.a. Bayesian historical data borrowing) method under clinical trial setting by calling 'Just Another Gibbs Sampler' ('JAGS') software. In addition, the 'BACCT' package evaluates user-specified decision rules by computing the type-I error/power, or probability of correct go/no-go decision at interim look. The evaluation can be presented numerically or graphically. Users need to have 'JAGS' 4.0.0 or newer installed due to a compatibility issue with 'rjags' package. Currently, the package implements the BAC method for binary outcome only. Support for continuous and survival endpoints will be added in future releases. We would like to thank AbbVie's Statistical Innovation group and Clinical Statistics group for their support in developing the 'BACCT' package.

Authors:Hongtao Zhang [aut, cre], Qi Tang [aut]

BACCT_1.0.tar.gz
BACCT_1.0.tar.gz(r-4.5-noble)BACCT_1.0.tar.gz(r-4.4-noble)
BACCT_1.0.tgz(r-4.4-emscripten)BACCT_1.0.tgz(r-4.3-emscripten)
BACCT.pdf |BACCT.html
BACCT/json (API)

# Install 'BACCT' in R:
install.packages('BACCT', repos = c('https://cran.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Uses libs:
  • jags– Just Another Gibbs Sampler for Bayesian MCMC
  • c++– GNU Standard C++ Library v3

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

3 exports 0.00 score 35 dependencies 9 scripts 222 downloads

Last updated 8 years agofrom:c915156148. Checks:OK: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKAug 25 2024
R-4.5-linuxOKAug 25 2024

Exports:BAC_binomdecision_evalheatmap_decision

Dependencies:clicodacolorspacefansifarverggplot2gluegtableisobandlabelinglatticelifecyclemagrittrMASSMatrixmgcvmunsellnlmepillarpkgconfigplyrR6RColorBrewerRcppreshape2rjagsrlangscalesstringistringrtibbleutf8vctrsviridisLitewithr