Thomas Laine E, Yang Siyun, Wojdyla Daniel, Schaubel Douglas E
Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina, USA.
Duke Clinical Research Institute, Duke University School of Medicine, Durham, North Carolina, USA.
Stat Med. 2020 Jul 30;39(17):2350-2370. doi: 10.1002/sim.8533. Epub 2020 Apr 3.
Observational studies of treatment effects attempt to mimic a randomized experiment by balancing the covariate distribution in treated and control groups, thus removing biases related to measured confounders. Methods such as weighting, matching, and stratification, with or without a propensity score, are common in cross-sectional data. When treatments are initiated over longitudinal follow-up, a target pragmatic trial can be emulated using appropriate matching methods. The ideal experiment of interest is simple; patients would be enrolled sequentially, randomized to one or more treatments and followed subsequently. This tutorial defines a class of longitudinal matching methods that emulate this experiment and provides a review of existing variations, with guidance regarding study design, execution, and analysis. These principles are illustrated in application to the study of statins on cardiovascular outcomes in the Framingham Offspring cohort. We identify avenues for future research and highlight the relevance of this methodology to high-quality comparative effectiveness studies in the era of big data.
治疗效果的观察性研究试图通过平衡治疗组和对照组的协变量分布来模拟随机实验，从而消除与测量到的混杂因素相关的偏差。加权、匹配和分层等方法，无论是否使用倾向得分，在横断面数据中都很常见。当在纵向随访中开始治疗时，可以使用适当的匹配方法来模拟目标实用试验。理想的感兴趣实验很简单；患者将依次入组，随机分配到一种或多种治疗方案，随后进行随访。本教程定义了一类模拟该实验的纵向匹配方法，并对现有变体进行了综述，同时提供了关于研究设计、实施和分析的指导。这些原则在应用于弗雷明汉后代队列中他汀类药物对心血管结局的研究时得到了说明。我们确定了未来研究的途径，并强调了该方法在大数据时代对高质量比较效果研究的相关性。
