Search

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

At a time when the health benefits of the Mediterranean diet (MD) are pervasively recognised, a paradoxical observation is the decreasing adherence to this dietary pattern in its native countries. This study aims to investigate temporal trends in adherence to the MD among adolescents (10–19 years old) in Lebanon. Data were drawn from three national cross-sectional surveys conducted at three points in time: 1997 (n 2004), 2009 (n 3656) and 2015 (n 1204). Dietary intake was assessed using 24-h dietary recalls, and adherence to the MD was assessed using two country-specific indexes: the composite Mediterranean diet (c-MED) index and Lebanese Mediterranean diet (LMD) index. Significant decreases in c-MED and LMD scores and in the proportion of adolescents adhering to the MD were observed between 1997 and 2015, with more consistent results among females (P < 0·05). Projections for the year 2030 showed further decreases, with less than a quarter of adolescents remaining adherent to the MD. Based on linear regression analyses, belonging to the year 2009 was associated with significantly lower MD scores compared with 1997, even after adjustment for potential covariates (c-MED β = –0·16, 95 % CI –0·30, –0·01; LMD β = –0·42, 95 % CI –0·67, –0·17). Similar results were obtained when comparing survey year 2015 with 1997 (c-MED score β = –0·20, 95 % CI –0·33, –0·06; LMD score β = –0·60, 95 % CI –0·82, –0·37). Findings highlight the erosion of the MD among Lebanese adolescents and underline the need for a comprehensive food system approach that fosters the promotion of the MD as a nutritionally balanced and sustainable dietary pattern. [ABSTRACT FROM AUTHOR]