Use of Bayesian Markov Chain Monte Carlo methods to model cost-of-illness data

Cooper, Nicola J., Sutton, Alex J., Mugford, Miranda and Abrams, Keith R. (2003) Use of Bayesian Markov Chain Monte Carlo methods to model cost-of-illness data. Medical Decision Making, 23 (1). pp. 38-53. ISSN 1552-681X

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Abstract

It is well known that the modeling of cost data is often problematic due to the distribution of such data. Commonly observed problems include 1) a strongly right-skewed data distribution and 2) a significant percentage of zero-cost observations. This article demonstrates how a hurdle model can be implemented from a Bayesian perspective by means of Markov Chain Monte Carlo simulation methods using the freely available software WinBUGS. Assessment of model fit is addressed through the implementation of two cross-validation methods. The relative merits of this Bayesian approach compared to the classical equivalent are discussed in detail. To illustrate the methods described, patient-specific nonhealth-care resource-use data from a prospective longitudinal study and the Norfolk Arthritis Register (NOAR) are utilized for 218 individuals with early inflammatory polyarthritis (IP). The NOAR database also includes information on various patient-level covariates.

Item Type:	Article
Faculty \ School:	Faculty of Medicine and Health Sciences > Norwich Medical School
UEA Research Groups:	Faculty of Medicine and Health Sciences > Research Groups > Health Economics Faculty of Medicine and Health Sciences > Research Groups > Public Health and Health Services Research (former - to 2023)
Related URLs:	http://dx.doi.org/10.1177/0272989X022396...
Depositing User:	EPrints Services
Date Deposited:	25 Nov 2010 11:09
Last Modified:	23 Jul 2024 16:30
URI:	https://ueaeprints.uea.ac.uk/id/eprint/12419
DOI:	10.1177/0272989X02239653

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