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Subgroup analysis of randomized controlled trials (RCTs) constitutes animportant component of the drug development process in precision medicine. Inparticular, subgroup analysis of early phase trials often influences the designand eligibility criteria of subsequent confirmatory trials and ultimatelyimpacts which subpopulations will receive the treatment after regulatoryapproval. However, subgroup analysis is typically complicated by small samplesizes, which lead to substantial uncertainty about the subgroup-specifictreatment effects. In this work we explore the use of external control (EC)data to augment an RCT's subgroup analysis. We define and discuss harmonizedestimators of subgroup-specific treatment effects to leverage EC data whileensuring that the subgroup analysis is coherent with a primary analysis usingRCT data alone. Our approach modifies subgroup-specific treatment effectestimators obtained through popular methods (e.g., linear regression) appliedjointly to the RCT and EC datasets. We shrink these estimates so that theirweighted average is close to a robust estimate of the average treatment effectin the overall trial population based on the RCT data alone. We study theproposed harmonized estimators with analytic results and simulations, andinvestigate standard performance metrics. The method is illustrated with a casestudy in glioblastoma.

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