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Covariate Adjustment for the Win Odds: Application to Cardiovascular Outcomes Trials
Cyrill Scheidegger1, Simon Wandel2, Tobias Mütze2
1Seminar for Statistics, ETH Zurich, Zurich, Switzerland.
Statistics in Medicine
|May 16, 2026
Summary
Covariate adjustment can improve clinical trial precision and power using the win odds measure. This method, linked to the marginal probabilistic index, enhances estimators and power when covariates are prognostic, though it may slightly inflate Type I errors in small samples.
Area of Science:
- Biostatistics
- Clinical Trial Methodology
- Statistical Inference
Background:
- Covariate adjustment is crucial for enhancing precision and power in clinical trials.
- The application of covariate adjustment to the win odds, a treatment effect measure based on pairwise comparisons, has been unclear.
- The win odds measure, related to the win ratio, treats ties as half a win for each group.
Purpose of the Study:
- To establish a connection between the win odds and the marginal probabilistic index, for which covariate adjustment theory is well-developed.
- To demonstrate the feasibility and benefits of covariate adjustment for the win odds.
- To provide accessible theory and practical application of covariate adjustment for the win odds.
Main Methods:
- Establishing a theoretical link between the win odds and the marginal probabilistic index.
- Developing and applying covariate adjustment methods for the win odds.
- Validating the approach using synthetic data from the CANTOS trial, HF-ACTION trial data, and simulated data.
Main Results:
- Covariate adjustment for the win odds is feasible and can lead to more precise estimators.
- A potential gain in statistical power is observed when adjusting the win odds for prognostic baseline covariates.
- A slight inflation of the Type I error rate was noted for small sample sizes.
Conclusions:
- Covariate adjustment is a viable method to enhance the precision and power of the win odds in clinical trials.
- The proposed method offers advantages over unadjusted win odds, particularly when baseline covariates are prognostic.
- Careful consideration of sample size is warranted due to potential Type I error inflation in smaller studies.
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