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

Daniel Schwartz1,2, Riddhiman Saha1, Steffen Ventz3

  • 1Department of Biostatistics, Harvard T.H. Chan School of Public Health, United States.

Journal of the Royal Statistical Society. Series B, Statistical Methodology
|April 3, 2026
PubMed

Insights

External control data can improve subgroup analyses in early-phase drug trials, addressing small sample sizes. Harmonized estimators ensure subgroup treatment effects align with overall trial results for precision medicine.

Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Pharmacometrics

Background:

  • Subgroup analyses are crucial in precision medicine drug development, guiding trial design and treatment approval.
  • Small sample sizes in early-stage trials create uncertainty in subgroup-specific treatment effect estimates.

Purpose of the Study:

  • To explore the use of external control (EC) data to augment subgroup analyses in randomized controlled trials (RCTs).
  • To define and discuss "harmonized estimators" for subpopulation-specific treatment effects using EC data.

Main Methods:

  • Leveraging EC data to augment subgroup analyses within RCTs.
  • Developing "harmonized estimators" that modify existing subgroup estimates (e.g., from linear regression) to align with overall RCT results.
  • Ensuring the weighted average of harmonized subgroup estimates matches the RCT-only overall effect estimate.

Main Results:

  • The proposed harmonized estimators provide a method to incorporate EC data into RCT subgroup analyses.
  • Analytic results, simulations, and a case study in oncology demonstrate the performance of these estimators.
  • The weighted average of harmonized subgroup estimates is coherent with the overall RCT effect estimate.

Conclusions:

  • Harmonized estimators offer a robust approach to address small sample size limitations in RCT subgroup analyses by incorporating external control data.
  • This method enhances the reliability of subpopulation-specific treatment effect estimates, supporting precision medicine initiatives.
  • The approach is applicable to various statistical methods and has been validated through simulations and a real-world oncology case study.
Keywords:
historical controlmodel misspecificationreal world datashrinkage estimationsubgroup analysis

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