arXiv subjects

F. Perry Wilson

Publications and source records attributed to F. Perry Wilson.

3 recordsLinked to original sources

arXiv2026

Statistical inference with win statistics in cluster-randomized trials with composite outcomes

Win statistics have become increasingly popular for analyzing hierarchical composite endpoints in clinical trials, because they summarize treatment benefit through pairwise comparisons that respect the clinical importance order among outcome components. The win ratio, win odds, net benefit, and desirability of outcome ranking (DOOR) are all based on the same underlying pairwise comparison methodology and can complement one another to show the strength of the treatment effect. Despite recent progress on win statistics, statistical inference for win statistics in cluster randomized trials (CRTs) remains underdeveloped. In this paper, we provide a comprehensive survey of testing procedures for the win ratio, win odds, net benefit, and DOOR in parallel-arm CRTs with hierarchical composite outcomes. Then based on each win statistic, we compare different testing procedures, including Wald tests based on cluster rank sum statistics and bivariate clustered U-statistics, tests that use a cluster jackknife variance, a score permutation test, a permutation based procedure with analytical variance estimation, and likelihood ratio test derived from clustered jackknife estimates. Through simulation studies that consider varying scenarios such as different cluster sizes, intracluster correlations, and censoring-induced ties, we characterize the finite-sample type I error and power of each procedure across a range of practical settings with small and large numbers of clusters.We illustrate our methods by reanalyzing the Strategies to Reduce Injuries and Develop Confidence in Elders (STRIDE) pragmatic CRT, and implement all win statistics methods in the WinsCRT R package.

stat.ME↗

arXiv2026

Leveraging machine learning to estimate individualized treatment effects in cluster-randomized trials

Cluster-randomized trials (CRTs) are widely used to evaluate interventions delivered at the clinic, practice, or community level. Although standard analyses typically target average treatment effects, such summaries mask potentially meaningful variation in treatment response across individuals and clusters. This work addresses the estimation of conditional average treatment effects (CATEs) for continuous outcomes in two-arm parallel CRTs by defining causal estimands that incorporate both individual- and cluster-level baseline covariates while marginalizing over unobserved cluster heterogeneity. To estimate these quantities, we develop a unified framework based on mixed-effects machine learning, integrating and extending a range of existing approaches, including Bayesian additive regression trees with random effects, multilevel Bayesian causal forests, mixed-effects random forests, several mixed-effects gradient boosting procedures, and generalized additive mixed models, while incorporating cluster-specific random intercepts to account for within-cluster dependence. We evaluate these methods across diverse simulation scenarios and demonstrate their use in the Task Shifting and Blood Pressure Control in Ghana CRT, which investigates strategies for improving hypertension management. Drawing on these investigations, we provide practical guidance for applying mixed-effects machine learning to quantify treatment-effect heterogeneity in CRTs, together with reproducible code that enables investigators to implement all methods within a coherent workflow.

stat.ME↗

arXiv2023

Using Overlap Weights to Address Extreme Propensity Scores in Estimating Restricted Mean Counterfactual Survival Times

While the inverse probability of treatment weighting (IPTW) is a commonly used approach for treatment comparisons in observational data, the resulting estimates may be subject to bias and excessively large variance when there is lack of overlap in the propensity score distributions. By smoothly down-weighting the units with extreme propensity scores, overlap weighting (OW) can help mitigate the bias and variance issues associated with IPTW. Although theoretical and simulation results have supported the use of OW with continuous and binary outcomes, its performance with right-censored survival outcomes remains to be further investigated, especially when the target estimand is defined based on the restricted mean survival time (RMST)-a clinically meaningful summary measure free of the proportional hazards assumption. In this article, we combine propensity score weighting and inverse probability of censoring weighting to estimate the restricted mean counterfactual survival times, and propose computationally-efficient variance estimators. We conduct simulations to compare the performance of IPTW, trimming, and OW in terms of bias, variance, and 95% confidence interval coverage, under various degrees of covariate overlap. Regardless of overlap, we demonstrate the advantage of OW over IPTW and trimming methods in bias, variance, and coverage when the estimand is defined based on RMST.

stat.ME↗