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Abstract Details

Activity Number: 664
Type: Contributed
Date/Time: Thursday, August 2, 2012 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #304031
Title: Assessment of Parametric and Nonparametric Bayesian Hierarchical Models for Profiling Health Care Providers
Author(s): Xiaowei Yang*+ and Hongxia Yang and Dao Zhou and Qian Zhang
Companies: University of California at Davis and University of California at Davis and University of California at Davis and University of California at Davis
Address: One Shields Ave, Davis, 95616,
Keywords: Provider Profiling ; Ranking Hospitals ; Nonparametric Bayes ; Bayesian Hierarchical Models ; Quality of Care ; Semi-parametric Bayesian Models

In this talk, we introduce a set of Bayesian hierarchical modeling methods for evaluating hospitals and surgeons performing coronary artery bypass graft (CABG) surgery in California. Two semi-parametric Bayesian hierarchical models will be developed, one for clustering hospitals and surgeons using risk-adjusted operative mortality rates, and one for ranking and clustering hospitals and doctors simultaneously. Based on the CABG and simulated data, we compare the performances of our methods and other available strategies, Bayesian and frequentist, seen in the literature. We aim to demonstrate that our methods can handle multi-level data structure, model flexible conditional response distributions and missing values, provide better inferences and predication when relatively small numbers of patients are seen within some hospitals or surgeons. Compare with profiling methods that are based on hierarchical models with Gaussian-distributed random-effects, our models are highly flexible and adaptable and do not require strong assumptions such as uni-modal or symmetric distributions.

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