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Flexible random‐effects models using Bayesian semi‐parametric models: applications to institutional comparisons
Author(s) -
Ohlssen D. I.,
Sharples L. D.,
Spiegelhalter D. J.
Publication year - 2006
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.2666
Subject(s) - computer science , random effects model , bayesian probability , parametric statistics , variety (cybernetics) , masking (illustration) , data mining , parametric model , machine learning , artificial intelligence , statistics , meta analysis , medicine , mathematics , art , visual arts
Random effects models are used in many applications in medical statistics, including meta‐analysis, cluster randomized trials and comparisons of health care providers. This paper provides a tutorial on the practical implementation of a flexible random effects model based on methodology developed in Bayesian non‐parametrics literature, and implemented in freely available software. The approach is applied to the problem of hospital comparisons using routine performance data, and among other benefits provides a diagnostic to detect clusters of providers with unusual results, thus avoiding problems caused by masking in traditional parametric approaches. By providing code for Winbugs we hope that the model can be used by applied statisticians working in a wide variety of applications. Copyright © 2006 John Wiley & Sons, Ltd.