Groupe d’études et de recherche en analyse des décisions


Outlier Detection for a Hierarchical Bayes Model in a Study of Hospital Variation in Surgical Procedures

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One of the most important aspects of profiling health care providers or services is constructing a model that is flexible enough to allow for random variation. At the same time, we wish to identify those institutions that clearly deviate from the usual standard of care. Here we propose a hierarchical Bayes model to study the choice of surgical procedure for rectal cancer using data previously analyzed by Simons et al. (1997). Using hospitals as random effects, we construct a computationally simple graphical method for determining hospitals that are outliers; that is, they differ significantly from other hospitals of the same type in terms of surgical choice.

, 22 pages