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An R package for bayesian trial design using historical control data
Eggleston, B. S., Ibrahim, J. G., McNeil, B., & Catellier, D. (2021). BayesCTDesign: An R package for bayesian trial design using historical control data. Journal of Statistical Software, 100(21), 1-51. https://doi.org/10.18637/jss.v100.i21
This article introduces the R (R Core Team 2019) 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 on CRAN, 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 (pwe) outcomes. Power for two-sided hypothesis tests at a user defined alpha 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.