BayesCTDesign: An R Package for Bayesian Trial Design Using Historical Control Data

BayesCTDesign: An R Package for Bayesian Trial Design Using Historical Control Data

Bayesian statistics, clinical trials, historical controls, power prior, R

This article introduces the R package BayesCTDesign for two-arm randomized Bayesian
trial design using historical control data when available, and simple two-arm randomized Bayesian trial design when historical control data is not available. The package
BayesCTDesign, which is available from the Comprehensive R Archive Network, has two
simulation functions, historic_sim() and simple_sim() for studying trial characteristics under user-defined scenarios, and two methods print() and plot() for displaying
summaries of the simulated trial characteristics. The package BayesCTDesign works with
two-arm trials with equal sample sizes per arm. The package BayesCTDesign allows a
user to study Gaussian, Poisson, Bernoulli, Weibull, lognormal, and piecewise exponential outcomes. Power for two-sided hypothesis tests at a user-defined α is estimated via
simulation using a test within each simulation replication that involves comparing a 95%
credible interval for the outcome specific treatment effect measure to the null case value.
If the 95% credible interval excludes the null case value, then the null hypothesis is rejected, else the null hypothesis is accepted. In the article, the idea of including historical
control data in a Bayesian analysis is reviewed, the estimation process of BayesCTDesign
is explained, and the user interface is described. Finally, the BayesCTDesign is illustrated
via several examples.

Barry S. Eggleston

https://www.jstatsoft.org/article/view/v100i21

RTI International

November 2021

Fajar bagus W

PDF

English

Text