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

Nicola J. Cooper, PhD

Alex J. Sutton, MSc

Miranda Mugford, DPhil

Keith R. Abrams, PhD

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.

Key Words: Bayesian methods • cost of illness • cost analysis • inflammatory polyarthritis • cross-validation

Medical Decision Making, Vol. 23, No. 1, 38-53 (2003)
DOI: 10.1177/0272989X02239653
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