Specifying a target trial prevents immortal time bias and other self-inflicted injuries in observational analyses

By Miguel A. Hern'an, B. Sauer, S. Hernández-Díaz, Robert William Platt, I. Shrier. Journal of Clinical Epidemiology, 2016

869 citations recorded by the source corpus.

Many analyses of observational data are attempts to emulate a target trial. The emulation of the target trial may fail when researchers deviate from simple principles that guide the design and analysis of randomized experiments. We review a framework to describe and prevent biases, including immortal time bias, that result from a failure to align start of follow-up, specification of eligibility, and treatment assignment. We review some analytic approaches to avoid these problems in comparative effectiveness or safety research.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/f6d8063d2fe3327be967c825dc7639c6eb1117b7

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Citations in the corpus

• Supervised machine learning: A brief primer (2020, background): “However, causal inference can be applied to observational (i.e., non-randomized) data in a way that mimics a hypothetical randomized trial (Hernán et al., 2016).”