batss.surv: A fast and flexible framework in R to simulate Bayesian multi-arm multi-stage clinical trials with time-to-event outcomes

Abstract

Adaptive clinical trials with time-to-event endpoints are increasingly common. However, implementing such designs in the Bayesian framework has been challenging due to the lack of readily available software and the high computational burden associated with Markov Chain Monte Carlo (MCMC) methods often used for estimating the posterior distributions. Here we present a major extension for time-to-event endpoints to the Bayesian Adaptive Trial Simulator Software (BATSS) R package, enabling flexible and efficient simulation of Bayesian adaptive multi-arm multi-stage (MAMS) designs through a modular and scalable framework. We demonstrate that this extension to BATSS is a powerful tool for evaluating the operating characteristics of trials with time-to-event outcomes with flexible interim analysis schedules that incorporate common adaptive features.