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

2024-10-02

AI Summary

Bayesian adaptive clinical trials face limited adoption due to the scarcity of efficient, user-friendly simulation tools. To address this, we developed BATSS, an open-source R package enabling modular simulation under continuous, binary, Poisson, and negative binomial primary outcome distributions. It supports key adaptive features—including efficacy and futility stopping rules, as well as response-adaptive randomization. BATSS introduces the first integration of integrated nested Laplace approximation (INLA) for accelerated posterior computation, combined with cluster-based parallelization, covariate adjustment, and highly customizable adaptation logic. This design substantially enhances both computational efficiency and methodological flexibility: complex adaptive trial simulations—running thousands of replications—can be completed within minutes. The package enables accurate evaluation of operating characteristics, including statistical power and Type I error rate, and has been successfully deployed in prospective protocol validation for multicenter clinical trials.

Technology Category

• Search and Optimization: Sampling/Simulation-based Search
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Web Mining and Content Analysis: Web data generation and simulation
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems

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.

Problem

• Simulates Bayesian adaptive clinical trial designs
• Evaluates operating characteristics for various outcome types
• Incorporates common adaptations like stopping rules and randomization

Innovation

• Bayesian adaptive designs simulator for clinical trials
• Flexible framework supporting multiple outcome distributions
• Modular package with parallel processing and Laplace approximations