Emulating a Randomised Controlled Trial With Observational Data: An Introduction to the Target Trial Framework.

Area of Science:

• Epidemiology
• Biostatistics
• Clinical Trial Design

Background:

• Randomized controlled trials (RCTs) are the gold standard for evidence due to minimal confounding and direct causal effect estimation.
• Observational studies, while valuable, traditionally face challenges in establishing causality.
• The target trial framework offers a method to enhance the causal inference capabilities of observational studies.

Purpose of the Study:

• To describe the target trial framework for emulating randomized controlled trials (RCTs) using observational data.
• To illustrate how key RCT design elements can be successfully replicated in observational study designs.
• To demonstrate the potential for observational studies to produce causal effect estimates comparable to RCTs.

Main Methods:

• The review outlines the target trial framework, focusing on emulating core RCT design components.
• Key elements discussed include eligibility criteria, treatment assignment/randomization, time zero definition, outcome specification, follow-up periods, causal contrasts (e.g., intention-to-treat vs. per-protocol), and statistical analyses.
• An example target trial emulating the TRITON-TIMI 38 trial is presented to compare effect estimates.

Main Results:

• Careful application of the target trial framework in observational studies allows for the emulation of RCT design principles.
• This emulation facilitates the estimation of causal treatment effects from observational data.
• Effect estimates derived from well-designed target trials using observational data can be comparable to those from actual RCTs.

Conclusions:

• The target trial framework provides a robust methodology for strengthening causal inference in observational research.
• By meticulously designing observational studies to mimic RCTs, researchers can generate high-quality evidence.
• This approach broadens the utility of real-world data for estimating causal treatment effects, complementing traditional RCT findings.