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Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
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Subgroup analysis methods for time-to-event outcomes in heterogeneous randomized controlled trials.

Valentine Perrin1, Nathan Noiry2, Nicolas Loiseau2

  • 1Owkin Inc., New York, USA. valentine.perrin@owkin.com.

BMC Medical Research Methodology
|February 26, 2026
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Summary

Identifying patient subgroups who respond to treatments is crucial for precision medicine. This study evaluates methods for time-to-event data, recommending interaction tests for heterogeneity detection and CATE estimation for subgroup identification.

Keywords:
BenchmarkingHeterogeneityRandomized controlled trialsSubgroup analysisTime-to-event

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Area of Science:

  • Biostatistics
  • Clinical Trial Analysis
  • Precision Medicine

Background:

  • Randomized controlled trials (RCTs) with non-significant results may obscure patient subgroups benefiting from experimental drugs, impeding drug development.
  • Identifying heterogeneous treatment effects is vital for precision medicine, yet systematic evaluations for time-to-event data are lacking.
  • Existing benchmarks primarily focus on binary and continuous endpoints, leaving a gap in understanding subgroup analysis for survival data.

Purpose of the Study:

  • To systematically evaluate subgroup analysis algorithms for time-to-event outcomes.
  • To address key questions: Is there treatment heterogeneity? Which biomarkers predict it? Who are the good responders?
  • To introduce a novel synthetic and semi-synthetic data generation process for controlled heterogeneity scenario exploration.

Main Methods:

  • Evaluation of various subgroup analysis algorithms applied to time-to-event data.
  • Utilizing a new data generation process to simulate diverse heterogeneity levels.
  • Assessing methods for detecting heterogeneity, identifying predictive biomarkers, and identifying responder subgroups.

Main Results:

  • Interaction test-based methods show superior statistical power for detecting subtle heterogeneity.
  • Cox-based multivariate and interaction test methods excel at identifying heterogeneity-predictive variables.
  • Methods estimating Conditional Average Treatment Effect (CATE), like S-learners, effectively identify responder subgroups, with Cox-multivariate performing well in low-to-intermediate heterogeneity.

Conclusions:

  • No single method is optimal for all heterogeneity investigations; method selection depends on the specific research question.
  • Recommend interaction test-based methods for heterogeneity detection and biomarker identification.
  • Advocate for a two-step approach: first, establish heterogeneity and identify covariates using interpretable methods, then use complex methods for subgroup identification.