Covariate-Adjusted Response-Adaptive Design with Delayed Outcomes

• 成果类型: Article; Early Access
• 署名作者: Ma, Xinwei; Wang, Jingshen; Wei, Waverly
• 署名单位: University of California System; University of California San Diego; University of California System; University of California Berkeley; University of Southern California
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459; 1537-274X
• DOI: 10.1080/01621459.2025.2604314
• 发表日期: 2026-05-21
• 关键词: Delayed outcomes; Frequentist adaptive experimental design; Response adaptive designs; EFFICIENT SEMIPARAMETRIC ESTIMATION; biased coin designs; asymptotic properties; clinical-trials; allocation; inference

摘要

Covariate-adjusted response-adaptive (CARA) designs have gained widespread adoption for their clear benefits in enhancing experimental efficiency and participant welfare. These designs dynamically adjust treatment allocations during interim analyses based on participant responses and covariates collected during the experiment. However, delayed responses can significantly compromise the effectiveness of CARA designs, as they hinder timely adjustments to treatment assignments when certain participant outcomes are not immediately observed. In this article, we propose a fully forward-looking CARA design that dynamically updates treatment assignments throughout the experiment as response delay mechanisms are progressively estimated. Our design strategy is informed by novel semiparametric efficiency calculations that explicitly account for outcome delays in a multi-stage setting. Through both theoretical investigations and simulation studies, we demonstrate that our proposed design offers a robust solution for handling delayed outcomes in CARA designs, yielding significant improvements in both statistical power and participant welfare. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.