Abstract
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.
Highlights
BayesCTDesignA controlled clinical trial is “an experiment performed on human subjects to assess the efficacy of a new treatment for some condition” (Matthews 2006, p. 1)
The hctrial package can help in designing trials with historical controls, but it is designed only for binary outcomes. bayesDP allows for historical control data to be incorporated using a power prior where a0, a parameter that determines how much of the information in the historical data is embedded in the power prior, is determined dynamically using a discount function
Before we get into the details of BayesCTDesign and its use, we will review Bayesian estimation with inclusion of historical control data by going through the computational concepts involved in Bayesian estimation with historical data and a power prior, as well as go through a simple mathematical example
Summary
A controlled clinical trial is “an experiment performed on human subjects to assess the efficacy of a new treatment for some condition” (Matthews 2006, p. 1). The package BayesCTDesign gives the investigator a set of tools to select primary outcome type as well as address sample size and power issues within the context of a Bayesian randomized two-arm controlled trial, as well as address issues related to historical control utilization. Using a power prior and simulations, the package BayesCTDesign gives the investigator some tools to determine how data from historical controls should be utilized when available and gain an understanding of the vulnerability of the final design to inclusion of improper historical control data. The package BayesCTDesign is a set of simulation tools that can help a clinical trialist to plan Bayesian two-arm randomized clinical trials by estimating power and other operational characteristics such as type I error, treatment effect estimate and variance, bias, and mean square error (MSE). BayesCTDesign has the functionality for simple two-arm trials with no historical data, but its real strength is studying trial designs that incorporate historical control data
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