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Related Experiment Video

Updated: Apr 30, 2026

Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Predicting Win-Loss Probabilities for Composite Time-to-Event Outcomes Under The Proportional Win-Fractions

Lu Mao1

  • 1Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, Wisconsin, USA.

Statistics in Medicine
|April 28, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical model to better analyze composite time-to-event outcomes by accounting for ties. The enhanced approach provides more accurate, clinically interpretable predictions of win and loss probabilities, improving effect size evaluation.

Keywords:
hierarchical composite endpointsmodel checkingnet benefitrobust variance estimationwin ratio

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Area of Science:

  • Biostatistics
  • Clinical Trials Methodology
  • Survival Analysis

Background:

  • Composite time-to-event outcomes are common in clinical research.
  • The win ratio, a relative measure, can obscure context by ignoring ties.
  • Varying event and tie rates across covariates and time complicate interpretation.

Purpose of the Study:

  • To develop a statistical framework that provides a more complete understanding of covariate effects on composite outcomes.
  • To enable prediction of time-dependent win and loss probabilities on an absolute scale.
  • To complement the win ratio with tie-adjusted measures for effect size evaluation.

Main Methods:

  • Coupling the proportional win-fractions (PW) model with a time-to-first-event model (e.g., Cox model) to infer tie probabilities.
  • Utilizing robust variance estimation for uncertainty quantification.
  • Employing residual-based diagnostics for model fit refinement and assumption checking.

Main Results:

  • The proposed approach yields accurate and clinically interpretable predictions for time-dependent win and loss probabilities.
  • Demonstrated diminishing returns on absolute win-loss probabilities with increasing biomarker levels, despite a constant win ratio.
  • Highlighted that violations of the proportional win-fractions assumption can lead to biased predictions.

Conclusions:

  • The combined modeling approach offers a more comprehensive assessment of covariate effects on composite outcomes than the win ratio alone.
  • Model diagnostics are crucial for ensuring the validity of predictions and identifying potential assumption violations.
  • The methodology is implemented in the R package 'WR', available on GitHub and CRAN.