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Abstract Details
Activity Number:
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283
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, July 31, 2012 : 8:30 AM to 10:20 AM
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Sponsor:
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Health Policy Statistics Section
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Abstract - #304789 |
Title:
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Fitting Heavy-Tailed Distributions to Health Care Utilization by Parametric and Bayesian Methods
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Author(s):
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Joseph C Gardiner*+ and Zhehui Luo and Xiaoqin Tang and RV Ramamoorthi
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Companies:
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Michigan State University and Michigan State University and Geisinger Health System and Michigan State University
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Address:
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B601 West Fee Hall, East Lansing, MI, 48824,
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Keywords:
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Coxian phase-type model ;
Bayesian methods ;
Length of stay ;
Hospital cost ;
SAS Software ;
HCUP
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Abstract:
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We consider fitting parametric distributions to healthcare utilization data that often exhibit heavy tails. The 3-parameter generalized gamma distribution and its special cases, the lognormal, Weibull, and gamma are examined. Continuous gamma mixing of the Weibull distribution leads to the Burr distribution and its special cases, the log-logistic and Pareto distributions. For finite mixtures we consider Coxian-phase type distributions and mixtures of exponentials. Both maximum likelihood and Bayesian methods are presented with prescriptions for fitting these models using recent enhancements to SAS software. Comparisons between competing models are made using probability-probability plots, formal likelihood ratio tests for nested models, and Vuong test for strictly non-nested models. We provide a demonstration of these methods to fitting covariate models to hospital stays for acute myocardial infarction from the Nationwide Inpatient Sample (NIS) of the Healthcare Utilization Project (HCUP).
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