Harmonized Estimation of Subgroup-Specific Treatment Effects in Randomized Trials: The Use of External Control Data

TL;DR

This paper proposes a method to improve subgroup treatment effect estimates in RCTs by integrating external control data, ensuring coherence with overall trial results, and enhancing precision in subgroup analyses.

Contribution

It introduces harmonized estimators that combine RCT and external control data, making subgroup effects consistent with overall trial estimates and improving analysis reliability.

Findings

Harmonized estimators improve subgroup effect estimation accuracy.

Method maintains consistency with overall RCT effect estimates.

Simulation results demonstrate enhanced performance over traditional methods.

Abstract

Subgroup analyses of randomized controlled trials (RCTs) constitute an important component of the drug development process in precision medicine. In particular, subgroup analyses of early-stage trials often influence the design and eligibility criteria of subsequent confirmatory trials and ultimately influence which subpopulations will receive the treatment after regulatory approval. However, subgroup analyses are often complicated by small sample sizes, which leads to substantial uncertainty about subgroup-specific treatment effects. We explore the use of external control (EC) data to augment RCT subgroup analyses. We define and discuss harmonized estimators of subpopulation-specific treatment effects that leverage EC data. Our approach can be used to modify any subgroup-specific treatment effect estimates that are obtained by combining RCT and EC data, such as linear regression. We alter these subgroup-specific estimates to make them coherent with a robust estimate of the average effect in the randomized population based only on RCT data. The weighted average of the resulting subgroup-specific harmonized estimates matches the RCT-only estimate of the overall effect in the randomized population. We discuss the proposed harmonized estimators through analytic results and simulations, and investigate standard performance metrics. The method is illustrated with a case study in oncology.