title: Simulation sample sizes for Monte Carlo partial EVPI calculations.
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.
authors.affiliation: Department of Probability and Statistics, University of Sheffield, The Hicks Building, Hounsfield Road, Sheffield, S3 7RH, UK. j.oakley@sheffield.ac.uk <j.oakley@sheffield.ac.uk>
authors.forename: Jeremy E
authors.initials: JE
authors.lastname: Oakley
authors.forename: Alan
authors.initials: A
authors.lastname: Brennan
authors.forename: Paul
authors.initials: P
authors.lastname: Tappenden
authors.forename: Jim
authors.initials: J
authors.lastname: Chilcott
mesh_terms: D000465:Algorithms; D015982:Bias; D016001:Confidence Intervals; D003362:Cost-Benefit Analysis; D003663:Decision Trees; D017048:Health Care Costs; D015233:Models, Statistical; D009010:Monte Carlo Method; D015999:Multivariate Analysis; D018401:Sample Size; D035501:Uncertainty