Bayesian Design and Analysis of Precision Trials with Partial Borrowing

TL;DR

This paper develops a Bayesian framework for designing and analyzing precision clinical trials that incorporate external data with partial relevance, improving effect heterogeneity estimation in sparse subgroups.

Contribution

It introduces a weighted Bayesian model for partial borrowing from external data and a design framework using external data-derived priors, tailored for precision medicine trials.

Findings

Weighted external data improves subgroup effect estimation.

Simulation shows the model outperforms dynamic borrowing in various scenarios.

Design framework effectively guides sample size and decision boundaries.

Abstract

With the advancement of precision medicine there is an increasing need for design and analysis methods in clinical trials with the objective of investigating effect heterogeneity and estimating subgroup effects. As this requires precise estimation of interaction effects, borrowing information from external data sources including retrospective studies and early phase clinical trials to enrich the trial in sparse subgroups is pertinent. Motivated by a trial in gastric cancer we consider a practical design and analysis framework for borrowing from external data sources that only partially inform the inference. As the analysis model we propose an individually weighted model where the external data are weighted based on their fit with the target population based on the distribution of a set of covariates. In a simulation study we assess the performance of the model under various scenarios and make comparisons to dynamic borrowing. In addition, we provide a Bayesian design framework where design priors are extracted from the external data to determine decision boundaries and sample sizes. The design procedure is demonstrated within the context of our motivating example.