Williams Nicholas T, Hoffman Katherine L, Díaz Iván, Rudolph Kara E
Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY 10032, United States.
Division of Biostatistics, Department of Population Health Sciences, Grossman School of Medicine, New York University, New York, NY 10016, United States.
Am J Epidemiol. 2024 Dec 2;193(12):1768-1775. doi: 10.1093/aje/kwae122.
Investigators often report estimates of the average treatment effect (ATE). While the ATE summarizes the effect of a treatment on average, it does not provide any information about the effect of treatment within any individual. A treatment strategy that uses an individual's information to tailor treatment to maximize benefit is known as an optimal dynamic treatment rule (ODTR). Treatment, however, is typically not limited to a single point in time; consequently, learning an optimal rule for a time-varying treatment may involve not just learning the extent to which the comparative treatments' benefits vary across the characteristics of individuals, but also learning the extent to which the comparative treatments' benefits vary as relevant circumstances evolve within an individual. The goal of this paper is to provide a tutorial for estimating ODTR from longitudinal observational and clinical trial data for applied researchers. We describe an approach that uses a doubly robust unbiased transformation of the conditional ATE. We then learn a time-varying ODTR for when to increase buprenorphine-naloxone dose to minimize a return to regular opioid use among patients with opioid use disorder. Our analysis highlights the utility of ODTRs in the context of sequential decision-making: The learned ODTR outperforms a clinically defined strategy. This article is part of a Special Collection on Pharmacoepidemiology.
研究人员经常报告平均治疗效果（ATE）的估计值。虽然ATE总结了治疗的平均效果，但它没有提供任何关于任何个体内部治疗效果的信息。一种利用个体信息来定制治疗以最大化益处的治疗策略被称为最优动态治疗规则（ODTR）。然而，治疗通常不限于单个时间点；因此，学习随时间变化的治疗的最优规则可能不仅涉及了解比较治疗的益处随个体特征变化的程度，还涉及了解比较治疗的益处随个体内部相关情况演变而变化的程度。本文的目的是为应用研究人员提供一个从纵向观察数据和临床试验数据估计ODTR的教程。我们描述了一种使用条件ATE的双重稳健无偏变换的方法。然后，我们为何时增加丁丙诺啡-纳洛酮剂量以尽量减少阿片类药物使用障碍患者恢复常规阿片类药物使用的情况学习一个随时间变化的ODTR。我们的分析突出了ODTR在序贯决策背景下的效用：所学习的ODTR优于临床定义的策略。本文是药物流行病学特刊的一部分。
