The Target Trial Framework for Causal Inference From Observational Data: Why and When Is It Helpful?

Area of Science:

• Epidemiology
• Biostatistics
• Health Services Research

Background:

• Randomized trials are the gold standard for determining intervention effectiveness and safety.
• Observational data is often used when randomized trials are not feasible or ethical.
• Causal inference from observational data presents challenges due to potential biases.

Purpose of the Study:

• To introduce and explain the target trial framework for causal inference using observational data.
• To discuss the utility, scope, and advantages of emulating target trials.
• To clarify the limitations of the target trial framework, particularly concerning data quality.

Main Methods:

• The target trial framework involves two steps: 1) defining a hypothetical target trial protocol and 2) emulating this trial using observational data.
• This emulation aims to mimic the design of a randomized pragmatic trial.
• The approach focuses on preventing common biases in observational analyses.

Main Results:

• The target trial framework improves the quality of observational analyses by addressing design-related biases.
• It helps reduce ambiguity in causal questions, leading to clearer effect estimates.
• The framework is advantageous in settings where randomized trials cannot fill evidence gaps.

Conclusions:

• Emulating target trials with observational data is a valuable strategy for causal inference when randomized trials are absent.
• This framework enhances the validity of observational studies but does not compensate for inherent data limitations.
• Adopting the target trial approach can generate reliable effect estimates and inform clinical and policy decisions.