#5-75E. Uncovering Institutional Variations: A Clinician's Guide to Multilevel Logistic Regression (Stat Med, 2017)

When evaluating multicenter clinical outcomes, standard logistic regression fails to separate individual patient risks from genuine institutional performance differences. This tutorial by Austin and Merlo demonstrates advanced multilevel logistic regression methods, including variance partition coefficients and median odds ratios, that properly account for patient clustering and quantify general contextual effects. Although its practical example is limited to a retrospective Ontario myocardial infarction database, this framework addresses critical limitations of traditional regression models. Mastering these metrics enables clinician-researchers to critically evaluate hospital quality variations and interpret observational multicenter studies with greater rigor in daily practice.

Citation: Peter C. Austin, Juan Merlo. Intermediate and advanced topics in multilevel logistic regression analysis. Statistics in Medicine. 2017. DOI: 10.1002/sim.7336

URL: https://doi.org/10.1002/sim.7336

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