A Bayesian adaptive enrichment design using aggregate historical data to inform individualized treatment recommendations

TL;DR

This paper introduces a Bayesian adaptive enrichment trial design that leverages aggregate historical data to improve individualized treatment recommendations, enhancing efficiency and decision-making in clinical studies.

Contribution

It develops a novel Bayesian method using normalized power priors for borrowing information from summary data, addressing subgroup-specific parameter identifiability issues.

Findings

Improved statistical power and early stopping capabilities.

Reduced sample size compared to non-borrowing designs.

Demonstrated effectiveness through simulation studies.

Abstract

Adaptive enrichment trials aim to identify and recruit participants most likely to benefit from treatment based on evolving biomarker evidence, with the goal of informing individualized treatment recommendations. Bayesian methods are well suited to these designs because they allow external information to be incorporated in a principled manner. In practice, prior studies often provide only summary-level information, with subgroup-specific estimates unavailable due to design or privacy constraints. Existing dynamic borrowing approaches therefore rely on aggregate measures, such as the average treatment effect, and implicitly assume that historical information maps directly onto model parameters. In adaptive enrichment settings aimed at identifying individualized treatment effects, however, subgroup-specific treatment parameters are not identifiable when only marginal historical effects are available. To address this gap, we propose a Bayesian adaptive enrichment design that borrows information from external studies using a normalized power prior anchored on one or more summary measures, such as the average treatment effect. Interim analyses use posterior probabilities to guide early stopping for efficacy or futility, or to continue recruitment within promising biomarker-defined subgroups. Simulation studies evaluate operating characteristics across historical bias, sample size, and prior informativeness. Together with a motivating future trial in obstructive sleep apnea, the results show efficiency gains versus non-borrowing designs, including improved power, earlier stopping, and reduced expected sample size.