Martinuka Oksana, le Cessie Saskia, Wolkewitz Martin
Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany.
Department of Clinical Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.
Clin Microbiol Infect. 2025 Apr 29. doi: 10.1016/j.cmi.2025.04.027.
During the COVID-19 pandemic, real-world data and observational studies played an important role in assessing treatment effectiveness. Methodological challenges such as confounding, immortal time bias, and competing risks were observed. Target trial emulation provides a structured framework for evaluating treatment effectiveness using observational data while mitigating these biases.
To describe common biases in observational COVID-19 research, introduce the target trial emulation framework, and discuss how these biases can be addressed in this framework. Specifically, we discuss the clone-censor-weight approach and provide real-world study examples demonstrating its application in COVID-19 research.
We summarise key principles of target trial emulation and the clone-censor-weight approach using published methodological articles. Additionally, we demonstrate the practical implementation by reviewing three studies that emulated a target trial to evaluate the effects of treatments in patients with COVID-19. These studies were selected without a predefined search strategy.
We define and discuss confounding, immortal time bias, and competing risks in studies using observational data. To facilitate the understanding of these biases, we use a hypothetical example evaluating the effects of hydroxychloroquine in hospitalised patients with COVID-19. We provide an overview of the target trial emulation framework and its core elements, explaining how it can mitigate these challenges. To illustrate the clone-censor-weight approach, we describe published examples demonstrating its application during the COVID-19 pandemic.
Target trial emulation is an important framework for evaluating treatment effects using observational data, but it requires careful implementation to mitigate methodological biases. Identifying and addressing confounding, immortal time bias, and competing risks during study design and analysis are important in any causal study evaluating treatment effects. This framework can improve the quality of observational studies and complement evidence from clinical trials, particularly when evidence is urgently needed, as during the first waves of the COVID-19 pandemic.
在新冠疫情期间，真实世界数据和观察性研究在评估治疗效果方面发挥了重要作用。观察到了诸如混杂、不朽时间偏倚和竞争风险等方法学挑战。目标试验模拟为利用观察性数据评估治疗效果提供了一个结构化框架，同时减轻这些偏倚。
描述新冠观察性研究中的常见偏倚，介绍目标试验模拟框架，并讨论如何在该框架中解决这些偏倚。具体而言，我们讨论克隆审查加权法，并提供真实世界研究实例，展示其在新冠研究中的应用。
我们使用已发表的方法学文章总结目标试验模拟和克隆审查加权法的关键原则。此外，我们通过回顾三项模拟目标试验以评估新冠患者治疗效果的研究来展示实际应用。这些研究是在没有预定义检索策略的情况下挑选出来的。
我们定义并讨论使用观察性数据的研究中的混杂、不朽时间偏倚和竞争风险。为便于理解这些偏倚，我们使用一个假设示例来评估羟氯喹对新冠住院患者的效果。我们概述目标试验模拟框架及其核心要素，解释它如何减轻这些挑战。为说明克隆审查加权法，我们描述已发表的实例，展示其在新冠疫情期间的应用。
目标试验模拟是利用观察性数据评估治疗效果的一个重要框架，但需要谨慎实施以减轻方法学偏倚。在任何评估治疗效果的因果研究中，在研究设计和分析过程中识别并解决混杂、不朽时间偏倚和竞争风险都很重要。这个框架可以提高观察性研究的质量，并补充临床试验的证据，特别是在像新冠疫情第一波期间那样急需证据的时候。
