Implementation of the trial emulation approach in medical research: a scoping review

BMC Medical Research Methodology

Research

Giulio Scola¹  Sabine Landau¹  Richard Emsley¹  Anca Chis Ster¹  Daniel Bean²  Nilesh Pareek³ 

[1] Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK;Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK;Health Data Research UK London, Institute of Health Informatics, University College London, London, UK;King’s College Hospital NHS Foundation Trust, London, UK;School of Cardiovascular and Metabolic Medicine & Sciences, BHF Centre of Excellence, King’s College London, London, UK;

关键词: Causal inference;    Target trial;    Trial emulation;    Observational data;   

DOI  :  10.1186/s12874-023-02000-9

 received in 2022-11-16, accepted in 2023-07-25,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundWhen conducting randomised controlled trials is impractical, an alternative is to carry out an observational study. However, making valid causal inferences from observational data is challenging because of the risk of several statistical biases. In 2016 Hernán and Robins put forward the ‘target trial framework’ as a guide to best design and analyse observational studies whilst preventing the most common biases. This framework consists of (1) clearly defining a causal question about an intervention, (2) specifying the protocol of the hypothetical trial, and (3) explaining how the observational data will be used to emulate it.MethodsThe aim of this scoping review was to identify and review all explicit attempts of trial emulation studies across all medical fields. Embase, Medline and Web of Science were searched for trial emulation studies published in English from database inception to February 25, 2021. The following information was extracted from studies that were deemed eligible for review: the subject area, the type of observational data that they leveraged, and the statistical methods they used to address the following biases: (A) confounding bias, (B) immortal time bias, and (C) selection bias.ResultsThe search resulted in 617 studies, 38 of which we deemed eligible for review. Of those 38 studies, most focused on cardiology, infectious diseases or oncology and the majority used electronic health records/electronic medical records data and cohort studies data. Different statistical methods were used to address confounding at baseline and selection bias, predominantly conditioning on the confounders (N = 18/49, 37%) and inverse probability of censoring weighting (N = 7/20, 35%) respectively. Different approaches were used to address immortal time bias, assigning individuals to treatment strategies at start of follow-up based on their data available at that specific time (N = 21, 55%), using the sequential trial emulations approach (N = 11, 29%) or the cloning approach (N = 6, 16%).ConclusionDifferent methods can be leveraged to address (A) confounding bias, (B) immortal time bias, and (C) selection bias. When working with observational data, and if possible, the ‘target trial’ framework should be used as it provides a structured conceptual approach to observational research.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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