Enhancing knowledge of treatment effects by analysing observational data as a randomised trial
Authors:
Open Access
- 8 April 2025
- journal article
- review article
- Published by Taylor & Francis in Disability and Rehabilitation
- Vol. 47 (25) , 6745-6749
Abstract
Randomised controlled trials are the gold standard design to obtain knowledge of causal effects of treatments. However, not all causal questions can be answered by randomised trials for practical or ethical reasons. Target trial emulation is a conceptual framework and set of analysis methods to obtain knowledge of causal effects of treatments using observational data, and is particularly valuable when randomised trials are not feasible. In this Perspective, we outline the concepts and application of target trial emulation, showing how it can be used to minimise bias due to confounding at baseline and over the course of sustained treatments, and how causal per-protocol analysis can be performed to provide informative yet unbiased estimates of treatment effects. We summarise a recent target trial emulation that examined the effects of intensity of early rehabilitation for hip fracture on performance in activities of daily living as a worked example. We encourage investigators to explore the target trial framework, and apply it to answer causal questions when using observational data.Keywords
This publication has 42 references indexed in Scilit:
- Intention to treat and per protocol analyses: differences and similaritiesJournal of Clinical Epidemiology, 2024
- The target trial framework in clinical epidemiology: principles and applicationsJournal of Clinical Epidemiology, 2023
- Reporting of Observational Studies Explicitly Aiming to Emulate Randomized TrialsJAMA Network Open, 2023
- Target Trial EmulationJAMA, 2022
- Interventions for improving mobility after hip fracture surgery in adultsCochrane Database of Systematic Reviews, 2022
- Causal Directed Acyclic GraphsJAMA, 2022
- Introduction to computational causal inference using reproducible Stata, R, and Python code: A tutorialStatistics in Medicine, 2021
- Core concepts in pharmacoepidemiology: Violations of the positivity assumption in the causal analysis of observational data: Consequences and statistical approachesPharmacoepidemiology and Drug Safety, 2021
- Does water kill? A call for less casual causal inferencesAnnals of Epidemiology, 2016
- Methods for dealing with time‐dependent confoundingStatistics in Medicine, 2012