Efficient computation of partial expected value of sample information using Bayesian approximation

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