Problem
Research questions and friction points this paper is trying to address.
win statistics
composite survival outcomes
right censoring
inverse probability weighting
efficiency loss
Improving inverse probability of censoring weighting for win statistics with composite survival outcomes
🤖 AI Summary
This study addresses the inefficiency of existing inverse probability of censoring weighting (IPCW) methods for analyzing composite time-to-event endpoints with censoring, which arises from discarding patient pairs whose ordering cannot be definitively determined—a problem exacerbated under high censoring rates or long follow-up windows. The authors propose a novel weighted estimator that replaces hard win/loss assignments with conditional tie probabilities, thereby incorporating partially observable pairs as fractional contributions to recover lost information and enhance estimation efficiency. Built upon the asymptotic theory of two-sample U-statistics, the method integrates IPCW with nuisance-parameter-adjusted inference and provides closed-form sandwich variance estimators for the win ratio, net benefit, and odds ratio. Simulations demonstrate substantial efficiency gains across low to high censoring scenarios, and the approach is successfully applied in a reanalysis of a completed randomized clinical trial.
📝 Abstract
Win statistics, including the win ratio, net benefit, and win odds, summarize treatment effects on hierarchical composite endpoints by sequentially comparing patient pairs on component outcomes ordered by clinical importance, proceeding to lower priority components only when higher priority ones are tied. Restricting comparisons to a pre-specified clinical horizon yields well defined estimands separated from the censoring mechanism, and it is critically important to address right censoring during estimation. Existing inverse probability of censoring weighting methods discard indeterminate pairs entirely, incurring avoidable efficiency loss that grows with censoring and restriction horizon length. We propose a novel estimator that replaces the confirmation of higher priority ties with a conditional tie probability given the observed data, recovering partially observed pairs as fractional contributions. Large sample theory is developed based on two-sample U-statistics with estimated nuisance functions, and closed-form sandwich variance estimators are obtained for the win ratio, net benefit, and win odds under our new weighting scheme. Simulations demonstrate sizable efficiency gains growing sharply from light censoring to high censoring rate based on our new estimator, and we further apply our estimator to reanalyze a completed randomized clinical trial.
Innovation
Methods, ideas, or system contributions that make the work stand out.
inverse probability of censoring weighting
win statistics
composite survival outcomes
conditional tie probability
U-statistics
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