arXiv subjects

Huiman Barnhart

Publications and source records attributed to Huiman Barnhart.

3 recordsLinked to original sources

arXiv2026

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

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.

stat.ME↗

arXiv2026

Estimation and Inference for Win Measures with Multiple Ordinal Endpoints Subject to Missingness

Win measures, including the win ratio (WR), win odds (WO), net benefit (NB), and desirability of outcome ranking (DOOR), are increasingly used in randomized clinical trials with multiple hierarchical ordinal endpoints. In practice, however, one or more component endpoints may have missing data. The standard pairwise-comparison approach, which treats pairs with missing outcomes as ties, can produce biased estimates, even if the data are missing completely at random (MCAR). Although inverse probability of censoring weighting (IPCW) methods have been developed for censored survival endpoints, corresponding methods for addressing missing hierarchical ordinal endpoints are not yet available. To address this gap, we develop inverse probability weighting (IPW) and augmented IPW (AIPW) estimators for win measures with hierarchical ordinal endpoints subject to missing data, allowing missingness to depend on treatment assignment and baseline covariates. The IPW estimator corrects bias by reweighting complete observed outcomes using joint non-missingness probabilities involved in estimating the joint cell probabilities that define the win measures. The AIPW estimator additionally incorporates outcome modeling, improving efficiency and achieving double robustness. For inference, we derive closed-form variance estimators for both methods based on influence functions. Simulation studies show that the standard approach can be substantially biased, whereas the proposed IPW and AIPW estimators remain consistent with near-nominal coverage. Furthermore, the AIPW estimator is generally more efficient than IPW estimator. Applications to the SCOUT-CAP and ACTT-1 trials illustrate the practical utility of the proposed methods. An R package, WinMO, is provided for implementation.

stat.ME↗

arXiv2026

Estimation and Inference of the Win Ratio for Two Hierarchical Endpoints Subject to Censoring and Missing Data

The win ratio (WR) is a widely used metric to compare treatments in randomized clinical trials with hierarchically ordered endpoints. Counting-based approaches, such as Pocock's algorithm, are the standard for WR estimation. However, this algorithm treats participants with censored or missing data inadequately, which may lead to biased and inefficient estimates, particularly in the presence of heterogeneous censoring or missing data between treatment groups. Although recent extensions have addressed some of these limitations for hierarchical time-to-event endpoints, no existing methods -- aside from the computationally intensive multiple imputation approach -- can accommodate settings that include non-survival endpoints that are subject to missing data. In this paper, we propose a simple nonparametric maximum likelihood estimator (NPMLE) of WR for two hierarchical endpoints that are subject to censoring and missing data. Our method uses all observed data, avoids strong parametric assumptions, and comes with a closed-form asymptotic variance estimator. We demonstrate its performance using simulation studies and two data examples, based on the HEART-FID and ISCHEMIA trials. The proposed method provides a consistent estimator, improves estimation efficiency, and is robust under non-informative censoring and missing at random (MAR) assumptions, offering a flexible alternative to existing WR estimation methods. A user-friendly R package, WinRS, is available to facilitate implementation.

stat.ME↗