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Updated: Feb 6, 2026

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Rforce: Random Forests for Composite Endpoints.

Yu Wang1, Soyoung Kim1, Chien-Wei Lin1

  • 1Division of Biostatistics, Medical College of Wisconsin, Milwaukee, Wisconsin, USA.

Statistics in Medicine
|February 5, 2026
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Summary
This summary is machine-generated.

This study introduces Rforce, a novel random forest method for analyzing composite endpoints in medical research. Rforce effectively handles both non-fatal and terminal events, overcoming limitations of traditional first-event analysis.

Keywords:
composite endpointsproportional mean modelrandom forests

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Area of Science:

  • Biostatistics
  • Clinical Trials
  • Medical Informatics

Background:

  • Composite endpoints are crucial for evaluating treatment efficacy in medical research.
  • Analyzing only the time to the first event in composite endpoints leads to significant information loss.
  • Terminal events pose competing risks and are often overlooked in standard analyses.

Purpose of the Study:

  • To address limitations in analyzing composite endpoints, particularly nonlinear covariate effects.
  • To introduce a novel statistical method for handling both non-fatal and terminal events within composite endpoints.
  • To improve the comprehensive analysis of clinical outcomes in medical research.

Main Methods:

  • Development of a novel random forest approach for composite endpoints (Rforce).
  • Utilization of generalized estimating equations for tree building within Rforce.
  • Incorporation of pseudo-at-risk duration to manage dependent censoring from terminal events.

Main Results:

  • Rforce effectively analyzes composite endpoints including non-fatal and terminal events.
  • The method accounts for information loss inherent in traditional first-event analyses.
  • Simulation studies and real-world data confirmed Rforce's robust performance.

Conclusions:

  • Rforce offers an advanced solution for analyzing complex composite endpoints in clinical studies.
  • This method enhances the utilization of data by considering all events, not just the first.
  • Rforce provides a valuable tool for researchers investigating treatment efficacy with composite outcomes.