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Practical elements to consider when emulating a target trial.

Viet-Thi Tran¹, Raphaël Porcher¹, Elodie Perrodeau²

¹Center for Research in Epidemiology and Statistics (CRESS), Université Paris Cité and Université Sorbonne Paris Nord, INSERM, INRAE, Paris F-75004, France; Centre d'Epidemiologie clinique, AP-HP, Hôpital Hôtel Dieu, Paris F-75004, France.

Journal of Clinical Epidemiology

|March 1, 2026

Summary

This summary is machine-generated.

Target trial emulation uses observational data to mimic randomized controlled trials (RCTs), enabling causal inference for interventions. This guide clarifies emulation methods and key planning elements for researchers.

Keywords:

Bias Conduct Estimands Observational studies Randomized controlled trials Target trial emulation

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Area of Science:

Epidemiology
Biostatistics
Health Research Methodology

Background:

Randomized controlled trials (RCTs) are the gold standard for causal inference but can be infeasible or unethical.
Observational data offers a valuable alternative but requires rigorous methods to approximate RCT designs.
Target trial emulation provides a framework for analyzing observational data to yield RCT-comparable causal effect estimates.

Purpose of the Study:

To delineate the distinctions between randomized controlled trials and target trial emulations.
To outline essential considerations for the design and analysis of target trial emulations.
To guide researchers in planning robust emulation studies for causal inference.

Main Methods:

Emulation involves explicitly mirroring the design components of a target randomized controlled trial.
Key elements include defining the target trial, specifying the analytical approach, and addressing potential biases.
Utilizing observational data to simulate the treatment and outcome ascertainment processes of an RCT.

Main Results:

Well-designed target trial emulations can produce causal inference on intervention effects comparable to RCTs.
Understanding the differences between RCTs and emulations is crucial for accurate interpretation.
The framework aids in mitigating biases inherent in observational data analysis.

Conclusions:

Target trial emulation is a powerful methodology for causal inference using observational data.
Adherence to emulation principles enhances the validity and reliability of findings.
This approach supports evidence-based decision-making when RCTs are not viable.