Use of Bayesian Markov Chain Monte Carlo Methods to Model Cost-of-Illness Data

Nicola J. Cooper, Alex J. Sutton, Miranda Mugford and Keith R. Abrams

Medical Decision Making, 2003, vol. 23, issue 1, 38-53

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.

Date: 2003
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Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:sae:medema:v:23:y:2003:i:1:p:38-53

DOI: 10.1177/0272989X02239653

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