Principled type I error rate inflation in two-arm clinical trial designs with external control information borrowing

TL;DR

This paper proposes a principled method for controlling type I error inflation when borrowing external control information in two-arm clinical trials, balancing error control and power.

Contribution

It introduces an interpretable, adaptive approach linking prior-data conflict to TIE inflation, applicable to Normal and binomial outcomes, without relying on robust priors.

Findings

Analytical link between prior-data conflict and TIE inflation

Adaptive decision thresholds improve error control

Applicable to Normal and binomial data

Abstract

External information borrowing is often considered in order to improve a clinical trial's efficiency. The Bayesian approach borrows such external information by specifying an informative prior distribution. A potential issue with this procedure is that external and current information may conflict, but such inconsistency may not be predictable a priori. Robust prior choices are typically proposed to limit extreme worsening of operating characteristics (OCs) in these situations. However, trade-offs are still present and in general no power gains are possible if strict control of type I error (TIE) rate is desired. In this context, principled justifications for TIE rate inflation can be of interest. Here we investigate two-arm trials, with a focus on external/historical control information borrowing. We investigate frequentist OCs trade-offs and propose an interpretable approach for external information borrowing. The approach analytically links observed potential prior-data conflict with allowances for TIE rate inflation and power loss. The approach does not rely on a robust prior specification, but can instead be interpreted as an adaptive choice of Bayes test decision thresholds under the available informative prior. A development for both Normal and binomial outcomes is provided.