BACTA-GPT: An AI-Based Bayesian Adaptive Clinical Trial Architect

TL;DR

BACTA-GPT is a fine-tuned GPT-3.5 model that assists statisticians in designing Bayesian adaptive clinical trials, reducing technical barriers through natural language interaction and AI code generation.

Contribution

This paper introduces BACTA-GPT, a novel LLM-based tool that simplifies Bayesian adaptive trial design and implementation for clinical researchers.

Findings

Model can generate Bayesian trial models from natural language input

Successfully evaluated operating characteristics via simulations

Demonstrates potential to lower technical barriers in trial design

Abstract

Bayesian adaptive clinical trials offer a flexible and efficient alternative to traditional fixed-design trials, but their implementation is often hindered by the complexity of Bayesian computations and the need for advanced statistical programming expertise. The authors introduce a custom fine-tuned LLM designed to assist with this and lower barriers to adoption of Bayesian methods for adaptive clinical trials. This paper describes the development and fine-tuning of BACTA-GPT, a Large Language Model (LLM)-based tool designed to assist in the implementation of Bayesian Adaptive Clinical Trials. This engine uses GPT-3.5 as the underlying model and takes in Natural Language input from the Statistician or the Trialist. The fine-tuned model demonstrates a viable proof-of-concept in its objectives. Test case evaluations show that the model is capable of generating a fit-for-purpose Bayesian model for an adaptive trial and evaluate its operating characteristics via simulations using R and JAGS. The integration of AI code generation has significant potential to lower technical barriers for the design and implementation of Bayesian Adaptive trials. But they also require attention to important considerations regarding validation and quality control.