Use of Bayesian methods to model the SF-6D health state preference based data

BACKGROUND: Conventionally, models used for health state valuation data have been frequentists. Recently a number of researchers have investigated the use of Bayesian methods in this area. The aim of this paper is to put on the map of modelling a new approach to estimating SF-6D health state utility values using Bayesian methods. This will help health care professionals in deriving better health state utilities of the original UK SF-6D for their specialized applications. METHODS: The valuation study is composed of 249 SF-6D health states valued by a representative sample of the UK population using the standard gamble technique. Throughout this paper, we present four different models, including one simple linear regression model and three random effect models. The predictive ability of these models is assessed by comparing predicted and observed mean SF-6D scores, R(2)/adjusted R(2) and RMSE. All analyses were carried out using Bayesian Markov chain Monte Carlo (MCMC) simulation methods freely available in the specialist software WinBUGS. RESULTS: The random effects model with interaction model performs best under all criterions, with mean predicted error of 0.166, R(2)/adjusted R(2) of 0.683 and RMSE of 0.218. CONCLUSIONS: The Bayesian models provide flexible approaches to estimate mean SF-6D utility estimates, including characterizing the full range of uncertainty inherent in these estimates. We hope that this work will provide applied researchers with a practical set of tools to appropriately model outcomes in cost-effectiveness analysis.