Win Time In Favor of Treatment (WINFT) for Hierarchical Endpoints

2026-08-09

AI Summary

Existing win ratio methods struggle with non-monotonic, multi-level longitudinal outcomes and rely on strong modeling assumptions. This work proposes the WINFT (Weighted Integrated Nonparametric Functional Time) metric, which evaluates treatment efficacy by quantifying the cumulative time during which the treatment group exhibits superior health advantage over the control group. WINFT accommodates any number and type of hierarchical longitudinal endpoints, thereby overcoming the limitations of conventional win-time analyses that require monotonicity and specific model specifications. The proposed framework subsumes existing approaches as special cases and, built upon a U-statistic foundation, enables direct derivation of variance estimators and confidence intervals under independent censoring and missing-at-random mechanisms—eliminating the computational burden of bootstrap procedures. Simulations and applications to the ACTT-1 and HF-ACTION clinical trials demonstrate its robust performance, flexibility, and interpretability.

Abstract

Standard win statistics methods determine a win, loss, or tie for a pair of subjects based on their worst outcomes (up to the end of study) that may not fully utilize all patients' conditions or disease experience throughout the follow-up period. While the newly developed win-time statistics fully utilize all patients' longitudinal information, these statistics have been limited to time-to-event endpoints and require monotonic pattern of the events. As such, they are not applicable to any type nor number of hierarchical longitudinal endpoints. We propose the win time in favor of treatment (WINFT), a general measure for any hierarchical longitudinal endpoints, that summarizes the total time a subject in the treatment group spends in a more favorable health state than a subject in the control group. Unlike existing win time methods, the WINFT does not require the component outcomes to be monotone and does not rely on modeling assumptions for estimating state probabilities. This flexibility allows analysis of a complex and diverse set of endpoints, and includes existing win time methods as special cases. Moreover, the WINFT is estimated based on U-statistics, which provide direct framework for variance estimation and confidence interval derivation, under independent censoring and missing at random assumptions, without expensive bootstrapping. We examine the performance of the proposed WINFT estimation method through simulation studies, and illustrate the method using data from the ACTT-1 COVID-19 and HF-ACTION trials. Overall, the WINFT offers a flexible and interpretable estimand for assessing treatment in clinical trial data with complex longitudinal outcomes.

Problem

• hierarchical endpoints
• longitudinal data
• win statistics
• non-monotone outcomes
• clinical trials

Innovation

• WINFT
• hierarchical endpoints
• longitudinal data
• U-statistics
• non-monotone outcomes