A1423
Title: Covariate-adjusted win statistics in randomized clinical trials with ordinal outcomes
Authors: Zhiqiang Cao - Shenzhen Technology University (China) [presenting]
Scott Zuo - Northwestern University (United States)
Mary Ryan Baumann - University of Wisconsin - Madison (United States)
Kendra Plourde - Yale University (United States)
Patrick Heagerty - University of Washington (United States)
Guangyu Tong - Yale University (United States)
Fan Li - Yale University (United States)
Abstract: Ordinal outcomes are common in clinical settings where they often represent increasing levels of disease progression or different levels of functional impairment. To compare different intervention strategies in clinical trials, the direct use of ordinal logistic regression models may not be ideal for analyzing ranked outcomes. The focus is on representing the average treatment effect for ordinal outcomes via intrinsic pairwise outcome comparisons captured through win estimates. Recognizing the value of baseline covariate adjustment toward enhanced precision, propensity score weighting estimators are first developed, including both inverse probability weighting (IPW) and overlap weighting (OW), tailored to estimating win parameters. Furthermore, augmented weighting estimators are developed that leverage an additional ordinal outcome regression to potentially improve efficiency over weighting alone. Leveraging the theory of U-statistics, the asymptotic theory for all estimators is established, and closed-form variance estimators are derived. Through extensive simulations the enhanced efficiency of the weighted estimators over the unadjusted estimator is demonstrated, with the augmented weighting estimators showing a further improvement in efficiency except for extreme cases. Finally, the proposed methods are illustrated with the ORCHID trial, and the covariate adjustment methods are implemented in an R package winPSW to facilitate the practical implementation.
