我们旨在调查目标试验模拟 (Target Trial Emulation, TTE) 在因果推断中的应用，包括研究主题、常用策略和需要改进的问题。我们按照JBI指南和PRISMA-ScR清单进行了一个范围性综述。一个健康研究专家图书管理员在多个医学数据库中进行了搜索，并且两名独立的审阅者在COVIDENCE综述管理软件中完成了筛选和提取。我们的搜索结果得到了1240篇论文，其中96篇论文符合数据提取的条件。结果显示，TTE在2018年至2021年间的使用显著增加。研究主题各不相同，主要集中在癌症、心血管疾病、脑血管疾病和传染病上。然而，并不是所有论文都明确指定了生成稳健因果证据的三个关键组成部分：零时点、随机分配模拟和比较策略。我们从检索到的论文中发现了一些常见问题，主要限制包括残留混杂、推广有限和缺乏报告指南等需要改进的方面。对TTE框架的遵循程度不均存在，并且需要进一步改进以促进利用观察数据进行因果推断的应用。版权所有 © 2023 Elsevier Inc. 保留所有权利。

We aim to investigate the implementation of Target Trial Emulation (TTE) for causal inference, involving research topics, frequently-used strategies, and issues indicating the need for future improvements.We performed a scoping review by following the JBI guidance and PRISMA-ScR checklist. A heath research focused librarian searched multiple medical databases, and two independent reviewers completed screening and extraction within COVIDENCE review management software.Our search resulted in 1240 papers, of which 96 papers were eligible for data extraction. Results show a significant increase in the use of TTE in 2018 and 2021. The study topics varied and focused primarily on cancer, cardiovascular and cerebrovascular diseases, and infectious diseases. However, not all papers specified well all three critical components for generating robust causal evidence: time zero, random assignment simulation and comparison strategy. Some common issues were observed from retrieved papers, and key limitations include residual confounding, limited generalizability, and a lack of reporting guidance that need to be improved.Uneven adherence to the TTE framework exists, and future improvements are needed in order to progress applications using causal inference with observational data.Copyright © 2023 Elsevier Inc. All rights reserved.