Oakley, J.E., Brennan, A., Tappenden, P. and Chilcott, J. (2010) Simulation sample sizes for Monte Carlo partial EVPI calculations. Journal of Health Economics, 29 (3). pp. 468-477. ISSN 0167-6296Full text available as:
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.
|Copyright, Publisher and Additional Information:||© 2010 Elsevier. This is an author produced version of a paper subsequently published in Journal of Health Economics. Uploaded in accordance with the publisher's self-archiving policy.|
|Keywords:||Economic model; Expected value of perfect information; Monte Carlo estimation; Bayesian decision theory|
|Academic Units:||?? Sheffield.PAS ??
The University of Sheffield > Faculty of Medicine, Dentistry and Health (Sheffield) > School of Health and Related Research (Sheffield)
The University of Sheffield > Faculty of Science (Sheffield) > School of Mathematics and Statistics (Sheffield)
|Depositing User:||Miss Anthea Tucker|
|Date Deposited:||25 Jun 2010 08:36|
|Last Modified:||08 Feb 2013 17:00|
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