A fast, flexible simulation framework for Bayesian adaptive designs -- the R package BATSS

TL;DR

The paper introduces BATSS, a flexible R package that enables fast simulation and evaluation of Bayesian adaptive designs in clinical trials, accommodating various outcomes and adaptations.

Contribution

It presents BATSS, a new software tool that simplifies the simulation of Bayesian adaptive trial designs with extensive customization options.

Findings

Supports multiple outcome distributions (normal, binary, Poisson, negative binomial).

Enables evaluation of operating characteristics of adaptive designs.

Incorporates advanced computational methods like Laplace approximations.

Abstract

The use of Bayesian adaptive designs for randomised controlled trials has been hindered by the lack of software readily available to statisticians. We have developed a new software package (Bayesian Adaptive Trials Simulator Software - BATSS for the statistical software R, which provides a flexible structure for the fast simulation of Bayesian adaptive designs for clinical trials. We illustrate how the BATSS package can be used to define and evaluate the operating characteristics of Bayesian adaptive designs for various different types of primary outcomes (e.g., those that follow a normal, binary, Poisson or negative binomial distribution) and can incorporate the most common types of adaptations: stopping treatments (or the entire trial) for efficacy or futility, and Bayesian response adaptive randomisation - based on user-defined adaptation rules. Other important features of this highly modular package include: the use of (Integrated Nested) Laplace approximations to compute posterior distributions, parallel processing on a computer or a cluster, customisability, adjustment for covariates and a wide range of available conditional distributions for the response.