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.

中文翻译：

---

与时间依赖性治疗相匹配：回顾并期待。

对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，从而消除与测量混杂因素有关的偏见。带有或不带有倾向得分的加权，匹配和分层等方法在横截面数据中很常见。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。理想的理想实验很简单；患者将按顺序入组，随机接受一种或多种治疗，然后随访。本教程定义了一类用于模拟该实验的纵向匹配方法，并提供了有关现有变异的综述，并提供了有关研究设计，执行和分析的指导。这些原理已在应用他汀类药物研究弗雷明汉后代研究组的心血管预后中得到了说明。我们确定了未来研究的途径，并强调了该方法与大数据时代高质量比较有效性研究的相关性。