The implementation of target trial emulation for causal inference: a scoping review

仿真 因果推理 推论 计算机科学 医学 数据科学 人工智能 心理学 计量经济学 数学 社会心理学
作者
Hanxiao Zuo,Lin Yu,Sandy Campbell,Shelby Yamamoto,Yuan Yan
出处
期刊:Journal of Clinical Epidemiology [Elsevier]
卷期号:162: 29-37 被引量:27
标识
DOI:10.1016/j.jclinepi.2023.08.003
摘要

Abstract

Objectives

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.

Study Design and Setting

We performed a scoping review by following the Joanna Briggs Institute (JBI) guidance and Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist. A health research–focused librarian searched multiple medical databases, and two independent reviewers completed screening and extraction within covidence review management software.

Results

Our search resulted in 1,240 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.

Conclusion

Uneven adherence to the TTE framework exists, and future improvements are needed to progress applications using causal inference with observational data.
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