当进行随机对照试验不可行时，一种替代方法是进行观察性研究。然而，从观察数据中进行有效的因果推断具有挑战性，因为存在多种统计偏倚的风险。2016年，Hernán和Robins提出了“目标试验框架”，以指导最佳的观察性研究设计和分析，同时避免最常见的偏倚。该框架包括（1）明确定义有关干预措施的因果问题，（2）指定假设试验的方案，以及（3）解释如何使用观察数据来模拟它。本综述的目的是识别和审查所有医学领域中试验模拟研究的明确尝试。Embase、Medline和Web of Science数据库中搜索发表在英文中的截至2021年2月25日的试验模拟研究。从被认为符合综述的研究中提取以下信息：主题领域、他们利用的观察数据类型，以及他们用来处理以下偏倚的统计方法：（A）混杂偏倚，（B）永生时间偏倚和（C）选择偏倚。搜索结果得到617项研究，其中我们认为有38项符合综述的条件。这38项研究中，大多数集中在心脏病学、传染病或肿瘤学领域，绝大多数使用电子健康记录/电子医疗记录数据和队列研究数据。不同的统计方法被用来处理基线混杂和选择偏倚，主要是通过对混杂因素进行调整（N=18/49，37%）和倒数选择偏移权重（N=7/20，35%）来处理。不同的方法被用来处理永生时间偏倚，根据特定时间点可用的数据将个体分配给治疗策略（N=21，55%），使用连续试验模拟方法（N=11，29%）或克隆方法（N=6，16%）。不同的方法可以用来处理（A）混杂偏倚、（B）永生时间偏倚和（C）选择偏倚。在使用观察数据时，如果可能，应使用“目标试验”框架，因为它为观察研究提供了结构化的概念方法。© 2023. BioMed Central Ltd., part of Springer Nature.

When conducting randomised controlled trials is impractical, an alternative is to carry out an observational study. However, making valid causal inferences from observational data is challenging because of the risk of several statistical biases. In 2016 Hernán and Robins put forward the 'target trial framework' as a guide to best design and analyse observational studies whilst preventing the most common biases. This framework consists of (1) clearly defining a causal question about an intervention, (2) specifying the protocol of the hypothetical trial, and (3) explaining how the observational data will be used to emulate it.The aim of this scoping review was to identify and review all explicit attempts of trial emulation studies across all medical fields. Embase, Medline and Web of Science were searched for trial emulation studies published in English from database inception to February 25, 2021. The following information was extracted from studies that were deemed eligible for review: the subject area, the type of observational data that they leveraged, and the statistical methods they used to address the following biases: (A) confounding bias, (B) immortal time bias, and (C) selection bias.The search resulted in 617 studies, 38 of which we deemed eligible for review. Of those 38 studies, most focused on cardiology, infectious diseases or oncology and the majority used electronic health records/electronic medical records data and cohort studies data. Different statistical methods were used to address confounding at baseline and selection bias, predominantly conditioning on the confounders (N = 18/49, 37%) and inverse probability of censoring weighting (N = 7/20, 35%) respectively. Different approaches were used to address immortal time bias, assigning individuals to treatment strategies at start of follow-up based on their data available at that specific time (N = 21, 55%), using the sequential trial emulations approach (N = 11, 29%) or the cloning approach (N = 6, 16%).Different methods can be leveraged to address (A) confounding bias, (B) immortal time bias, and (C) selection bias. When working with observational data, and if possible, the 'target trial' framework should be used as it provides a structured conceptual approach to observational research.© 2023. BioMed Central Ltd., part of Springer Nature.