Location of Repository

Simulation sample sizes for Monte Carlo partial EVPI calculations

By J.E. Oakley, A. Brennan, P. Tappenden and J. Chilcott

Abstract

Partial expected value of perfect information (EVPI) quantifies the value of removing uncertainty about unknown parameters in a decision model. EVPIs can be computed via Monte Carlo methods. An outer loop samples values of the parameters of interest, and an inner loop samples the remaining parameters from their conditional distribution. This nested Monte Carlo approach can result in biased estimates if small numbers of inner samples are used and can require a large number of model runs for accurate partial EVPI estimates. We present a simple algorithm to estimate the EVPI bias and confidence interval width for a specified number of inner and outer samples. The algorithm uses a relatively small number of model runs (we suggest approximately 600), is quick to compute, and can help determine how many outer and inner iterations are needed for a desired level of accuracy. We test our algorithm using three case studies. (C) 2010 Elsevier B.V. All rights reserved

Publisher: Elsevier
Year: 2010
OAI identifier: oai:eprints.whiterose.ac.uk:10962

Suggested articles

Preview


To submit an update or takedown request for this paper, please submit an Update/Correction/Removal Request.