Sample size reassessment in Bayesian hybrid clinical trials

TL;DR

This paper introduces a Bayesian hybrid clinical trial design that uses an interim Hellinger distance-based criterion to reassess sample size and control variance, improving robustness when incorporating historical controls.

Contribution

A novel Bayesian two-arm trial method that adaptively reassesses sample size and control variance using a Hellinger distance measure at interim analysis.

Findings

Method is flexible for continuous and binary endpoints.

Demonstrates robustness to heterogeneity in historical controls.

Effective in maintaining trial validity with historical data.

Abstract

The use of historical controls offers a valuable alternative when traditional randomized controlled trials are not feasible. However, such approaches may introduce bias due to temporal changes in patient populations, diagnostic criteria, and/or treatment standards. Hybrid designs, which combine a concurrent control arm with historical control data, can help mitigate the possible bias. We propose a novel Bayesian two-arm randomized clinical trial design incorporating an interim analysis. At the interim analysis, a new criterion derived from the Hellinger distance is used to quantify the similarity between historical and concurrent control data outcomes. This measure informs both (1) the variance function of the control prior distribution in the final analysis and (2) the sample size reassessment for the second stage of the trial. The proposed approach is designed to accommodate both continuous and binary endpoints and is assessed through extensive simulation studies. Results demonstrate the method flexibility and robustness in adapting to varying degrees of historical-control heterogeneity.