Use of Bayesian Markov Chain Monte Carlo methods to model cost-of-illness data.
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 non-health-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.
Cooper
Nicola J
NJ
Department of Epidemiology and Public Health, University of Leicester, United Kingdom. njc21@le.ac.uk
Sutton
Alex J
AJ
Mugford
Miranda
M
Abrams
Keith R
KR
D001168:Arthritis / Q000191:economics; D001499:Bayes Theorem; D003198:Computer Simulation; D017281:Cost of Illness; D003365:Costs and Cost Analysis / Q000706:statistics & numerical data; D006801:Humans; D008390:Markov Chains; D017059:Models, Econometric; D009010:Monte Carlo Method; D015203:Reproducibility of Results