Key result
The target trial framework improves causal inference in observational perioperative and critical care outcomes research.
Why the study?
Randomized trials are not always available or feasible in perioperative and critical care, requiring researchers to understand how target trial emulation can and cannot avoid biases in observational studies.
The target trial framework provides a structured methodological approach to improve causal inference and avoid common biases in observational perioperative and critical care research.
Encourages cautious interpretation of observational perioperative data; extends causal inference tools for researchers.
Estimating effects of interventions is a central task in perioperative and critical care outcomes research. While randomized trials remain the accepted standard for causal inference, trial data are not always available to inform clinical decisions, and some questions cannot be answered feasibly or efficiently with trials. In these settings, studies using observational healthcare data may be used to inform practice. Causal inference from observational data has been reconsidered in recent years, challenging the prevailing notion among clinical researchers that causal conclusions cannot be drawn from observational studies. The "target trial framework" is one contribution within a growing methodologic field that helps investigators avoid common pitfalls in observational study design and analysis. Importantly, researchers must understand which biases this framework can-and cannot-help avoid. The authors present an overview of target trial emulation and describe the promise and limitations of this framework for improving observational perioperative and critical care outcomes research.
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Messinger et al. (2025) conducted a review in Perioperative and critical care outcomes. Target trial emulation framework was evaluated. The target trial framework offers a methodological approach to improve causal inference from observational data in perioperative and critical care outcomes research by avoiding common pitfalls.
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