The win odds: statistical inference and regression

James Song, Johan Verbeeck, Bo Huang, David C. Hoaglin, Margaret Gamalo-Siebers, Yodit Seifu, Duolao Wang, Freda Cooner, Gaohong Dong

Research output: Contribution to journalComment/debate

7 Citations (Scopus)

Abstract

Generalized pairwise comparisons and win statistics (i.e., win ratio, win odds and net benefit) are advantageous in analyzing and interpreting a composite of multiple outcomes in clinical trials. An important limitation of these statistics is their inability to adjust for covariates other than by stratified analysis. Because the win ratio does not account for ties, the win odds, a modification that includes ties, has attracted attention. We review and combine information on the win odds to articulate the statistical inferences for the win odds. We also show alternative variance estimators based on the exact permutation and bootstrap as well as statistical inference via the probabilistic index. Finally, we extend multiple-covariate regression probabilistic index models to the win odds with a univariate outcome. As an illustration we apply the regression models to the data in the CHARM trial.

Original languageEnglish
Pages (from-to)140-150
Number of pages11
JournalJournal of Biopharmaceutical Statistics
Volume33
Issue number2
Early online date10 Aug 2022
DOIs
Publication statusE-pub ahead of print - 10 Aug 2022

Keywords

  • bootstrap
  • net benefit
  • permutation
  • probabilistic index model
  • win odds
  • Win ratio
  • win statistics

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