Premium
Approximate models for aggregate data when individual‐level data sets are very large or unavailable
Author(s) -
Peköz Erol A.,
Shwartz Michael,
Christiansen Cindy L.,
Berlowitz Dan
Publication year - 2010
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.3979
Subject(s) - aggregate (composite) , matching (statistics) , computer science , bayesian probability , aggregate data , statistics , data mining , econometrics , mathematics , artificial intelligence , materials science , composite material
In this article, we study a Bayesian hierarchical model for profiling health‐care facilities using approximately sufficient statistics for aggregate facility‐level data when the patient‐level data sets are very large or unavailable. Starting with a desired patient‐level model, we give several approximate models and the corresponding summary statistics necessary to implement the approximations. The key idea is to use sufficient statistics from an approximate model fitted by matching up derivatives of the models' log‐likelihood functions. This derivative matching approach leads to an approximation that performs better than the commonly used approximation given in the literature. The performance of several approximation approaches is compared using data on 5 quality indicators from 32 Veterans Administration nursing homes. Copyright © 2010 John Wiley & Sons, Ltd.