Baldwin Faye D, Khalaf Rukun K S, Kolamunnage-Dona Ruwanthi, Jorgensen Andrea L
Department of Health Data Science, University of Liverpool, Liverpool L69 3GL, UK.
Department of Public Health, Policy and Systems, University of Liverpool, Liverpool L69 3GL, UK.
J Pers Med. 2025 May 10;15(5):195. doi: 10.3390/jpm15050195.
: Target trial emulation involves the application of design principles from randomised controlled trials (RCTs) to observational data, and is particularly useful in situations where an RCT would be unfeasible. Biomarker-guided trials, which incorporate biomarkers within their design to either guide treatment and/or determine eligibility, are often unfeasible in practice due to sample size requirements or ethical concerns. Here, we undertake a systematic review of methodologies used in target trial emulations, comparing treatment effectiveness, critically appraising them, and considering their applicability to the emulation of biomarker-guided trials. : A comprehensive search strategy was developed to identify studies reporting on methods for target trial emulation comparing the effectiveness of treatments using observational data, and applied to the following bibliographic databases: PubMed, Scopus, Web of Science, and Ovid MEDLINE. A narrative description of methods identified in the review was undertaken alongside a critique of their relative strengths and limitations. : We identified a total of 59 papers: 47 emulating a target trial ('application' studies), and 12 detailing methods to emulate a target trial ('methods' studies). A total of 25 papers were identified as emulating a biomarker-guided trial (42%). While all papers reported methods to adjust for baseline confounding, 40% of application papers did not specify methods to adjust for time-varying confounding. : This systematic review has identified a range of methods used to control for baseline, time-varying, and residual/unmeasured confounding within target trial emulation and provides a guide for researchers interested in emulation of biomarker-guided trials.
目标试验模拟涉及将随机对照试验(RCT)的设计原则应用于观察性数据,在RCT不可行的情况下尤其有用。生物标志物引导试验在设计中纳入生物标志物以指导治疗和/或确定入选标准,但由于样本量要求或伦理问题,在实践中往往不可行。在此,我们对目标试验模拟中使用的方法进行系统综述,比较治疗效果,对其进行批判性评估,并考虑它们对生物标志物引导试验模拟的适用性。
制定了全面的检索策略,以识别报告使用观察性数据比较治疗效果的目标试验模拟方法的研究,并应用于以下书目数据库:PubMed、Scopus、Web of Science和Ovid MEDLINE。对综述中确定的方法进行叙述性描述,并对其相对优势和局限性进行评论。
我们共识别出59篇论文:47篇模拟目标试验(“应用”研究),12篇详细介绍模拟目标试验的方法(“方法”研究)。共有25篇论文被确定为模拟生物标志物引导试验(42%)。虽然所有论文都报告了调整基线混杂因素的方法,但40%的应用论文未具体说明调整随时间变化的混杂因素的方法。
本系统综述确定了一系列用于在目标试验模拟中控制基线、随时间变化以及残余/未测量混杂因素的方法,并为对生物标志物引导试验模拟感兴趣的研究人员提供了指南。