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Partial expected value of perfect information (EVPI) calculations can quantify the value of learning about particular subsets of uncertain parameters in decision models. Published case studies have used different computational [...]

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jhealeco.2006.09.002 Byline: Samer A. Kharroubi (a), John E. Brazier (c), Jennifer Roberts (c)(d), Anthony O'Hagan (b) Keywords: Preference-based health measure; SF-6D; Nonparametric methods Abstract: This paper reports on the findings from applying a new approach to modelling health state valuation data. The approach applies a nonparametric model to estimate SF-6D health state utility values using Bayesian methods. The data set is the UK SF-6D valuation study where a sample of 249 states defined by the SF-6D (a derivative of the SF-36) was valued by a representative sample of the UK general population using standard gamble. The paper presents the results from applying the nonparametric model and comparing it to the original model estimated using a conventional parametric random effects model. The two models are compared theoretically and in terms of empirical performance. The paper discusses the implications of these results for future applications of the SF-6D and further work in this field. Author Affiliation: (a) Department of Mathematics, University of York, Heslington, York YO10 5DD, UK (b) Centre for Bayesian Statistics in Health Economics, Department of Probability and Statistics, University of Sheffield, Sheffield, UK (c) Health Economics and Decision Science, School of Health and Related Research, University of Sheffield, UK (d) Department of Economics, University of Sheffield, UK Article History: Received 25 July 2005; Revised 6 September 2006; Accepted 20 September 2006

It has long been recognised that respondent characteristics can impact on the values they give to health states. This paper reports on the findings from applying a non-parametric approach to estimate the covariates in a model of SF-6D health state values using Bayesian methods. The data set is the UK SF-6D valuation study, where a sample of 249 states defined by the SF-6D (a derivate of the SF-36) was valued by a sample of the UK general population using standard gamble. Advantages of the nonparametric model are that it can be used to predict scores in populations with different distributions of characteristics and that it allows for an impact to vary by health state (whilst ensuring that full health passes through unity). The results suggest an important age effect, with sex, class, education, employment and physical functioning probably having some effect, but the remaining covariates having no discernable effect. Adjusting for covariates in the UK sample made little difference to mean health state values. The paper discusses the implications of these results for policy. Keywords: Preference-based health measure; Covariates; Nonparametric Bayesian methods

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.jhealeco.2006.06.002 Byline: Alan Brennan, Samer A. Kharroubi Keywords: Bayesian decision theory; Value of information; Uncertainty; Sensitivity analysis; Sample size; Clinical trial design; Stochastic CEA; Approximate Bayesian inference Abstract: We describe a novel process for transforming the efficiency of partial expected value of sample information (EVSI) computation in decision models. Traditional EVSI computation begins with Monte Carlo sampling to produce new simulated data-sets with a specified sample size. Each data-set is synthesised with prior information to give posterior distributions for model parameters, either via analytic formulae or a further Markov Chain Monte Carlo (MCMC) simulation. A further 'inner level' Monte Carlo sampling then quantifies the effect of the simulated data on the decision. This paper describes a novel form of Bayesian Laplace approximation, which can be replace both the Bayesian updating and the inner Monte Carlo sampling to compute the posterior expectation of a function. We compare the accuracy of EVSI estimates in two case study cost-effectiveness models using 1st and 2nd order versions of our approximation formula, the approximation of Tierney and Kadane, and traditional Monte Carlo. Computational efficiency gains depend on the complexity of the net benefit functions, the number of inner level Monte Carlo samples used, and the requirement or otherwise for MCMC methods to produce the posterior distributions. This methodology provides a new and valuable approach for EVSI computation in health economic decision models and potential wider benefits in many fields requiring Bayesian approximation. Author Affiliation: School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, South Yorkshire S1 4DA, UK Article History: Received 17 September 2005; Revised 16 June 2006; Accepted 16 June 2006

A method to transform efficiency of partial expected value of sample information computation in decision models using Bayesian functions is proposed.

Partial expected value of perfect information calculations can quantify the value of learning about particular subsets of uncertain parameters in decision models. In this article, Brennan and colleagues study the [...]