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Generalized win-odds regression models for composite endpoints.

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  • 1Department of Statistics, University of Pittsburgh, Pittsburgh, PA, 15213, USA.

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Summary
This summary is machine-generated.

Win Odds (WO) regression models offer a novel approach to analyzing multiple clinical outcomes, providing a more nuanced understanding of treatment effects than traditional methods. These models enable hierarchical ordering of outcomes and robust analysis of complex data.

Keywords:
Functional response modelInverse probability weightingMultiple outcomesTreatment effectU-statisticWin statistics

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Area of Science:

  • Biostatistics
  • Clinical Trials
  • Statistical Modeling

Background:

  • Traditional time-to-first-event analysis treats multiple outcomes equally, irrespective of clinical importance.
  • Emerging methods like Win Ratio, Net Benefit, and Win Odds (WO) offer advanced handling of diverse outcomes and hierarchical ordering.

Purpose of the Study:

  • To propose proportional Win Odds (WO) regression models for evaluating treatment effects on multiple outcomes.
  • To provide an interpretable yet advanced statistical framework for complex clinical trial data.

Main Methods:

  • Development of proportional WO regression models using functional response modeling.
  • Construction of estimating equations based on all possible pairings of treated and control individuals.
  • Distinguishing informative ties from censoring and employing inverse probability of censoring weighting.

Main Results:

  • The proposed WO regression models are interpretable, akin to standard logistic regression.
  • The models can simultaneously analyze multiple outcomes of different types.
  • Asymptotic properties of regression coefficients are established using U-statistic theory.

Conclusions:

  • Proportional WO regression provides a flexible and powerful tool for analyzing multiple clinical outcomes.
  • The methodology offers a significant advancement over traditional time-to-first-event analyses in clinical research.
  • Numerical studies demonstrate the finite sample performance of the proposed models.