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Target trial emulation framework: mitigating methodological challenges and application in COVID-19 treatment
Oksana Martinuka1, Saskia le Cessie2, Martin Wolkewitz1
1Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany.
Background:
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.
Objectives:
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.
Sources:
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.
Content:
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.
Implications:
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.
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