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.
Authors: Thevaa Chandereng, Donald Musgrove, Tarek Haddad, Graeme Hickey, Timothy Hanson and Theodore Lystig
bayesCT is a R package for simulation and analysis of adaptive Bayesian randomzied controlled trials under a range of trial designs and outcome types. Currently, it supports Gaussian and binomial. The time-to-event data will be added shortly. The
bayesCT package website is available here. Please note this package is still under development.
Prior to analyzing your data, the R package needs to be installed. The easiest way to install
bayesCT is through CRAN:
There are other additional ways to download
bayesCT. The first option is most useful if want to download a specific version of
bayesCT (which can be found at https://github.com/thevaachandereng/bayesCT/releases):
devtools::install_github("thevaachandereng/[email protected]")devtools::install_version("bayesCT", version = "x.x.x", repos = "")
The second option is to download through GitHub:
After successful installation, the package must be loaded into the working space:
See the vignettes for usage instructions and example.
bayesCT is available under the open source GNU General Public License, version 3.