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The win odds: statistical inference and regression.

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|August 10, 2022
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Summary
This summary is machine-generated.

Generalized pairwise comparisons, including win odds, offer advantages for analyzing multiple clinical trial outcomes. This study enhances win odds by incorporating ties and extending regression models for covariate adjustment.

Keywords:
Win ratiobootstrapnet benefitpermutationprobabilistic index modelwin oddswin statistics

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Area of Science:

  • Clinical Trials Methodology
  • Biostatistics
  • Statistical Inference

Background:

  • Generalized pairwise comparisons and win statistics (win ratio, win odds, net benefit) are valuable for analyzing composite outcomes in clinical trials.
  • A key limitation of these statistics is the inability to adjust for covariates beyond stratified analysis.
  • The win odds statistic, which accounts for ties, has gained attention as an alternative to the win ratio.

Approach:

  • This work reviews and synthesizes information on win odds to clarify their statistical inferences.
  • Alternative variance estimators using exact permutation and bootstrap methods are presented.
  • Statistical inference is explored via the probabilistic index.

Key Points:

  • The study extends multiple-covariate regression probabilistic index models to the win odds for univariate outcomes.
  • Regression models are applied to data from the CHARM trial for illustration.
  • The enhanced win odds methodology provides a more robust approach to analyzing clinical trial data with covariates.

Conclusions:

  • The developed methods offer improved statistical inference for win odds in clinical trials.
  • Covariate adjustment is achieved through extended regression models, overcoming limitations of stratified analysis.
  • These advancements contribute to more comprehensive and accurate interpretation of composite outcomes in clinical research.