title: Estimating the expected value of partial perfect information: a review of methods.
abstract: BACKGROUND
  Value of information analysis provides a framework for the analysis of uncertainty within economic analysis by focussing on the value of obtaining further information to reduce uncertainty. The mathematical definition of the expected value of perfect information (EVPI) is fixed, though there are different methods in the literature for its estimation. In this paper these methods are explored and compared.
  
  
  METHODS
  Analysis was conducted using a disease model for Parkinson's disease. Five methods for estimating partial EVPIs (EVPPIs) were used: a single Monte Carlo simulation (MCS) method, the unit normal loss integral (UNLI) method, a two-stage method using MCS, a two-stage method using MCS and quadrature and a difference method requiring two MCS. EVPPI was estimated for each individual parameter in the model as well as for three groups of parameters (transition probabilities, costs and utilities).
  
  
  RESULTS
  Using 5,000 replications, four methods returned similar results for EVPPIs. With 5 million replications, results were near identical. However, the difference method repeatedly gave estimates substantially different to the other methods.
  
  
  CONCLUSIONS
  The difference method is not rooted in the mathematical definition of EVPI and is clearly an inappropriate method for estimating EVPPI. The single MCS and UNLI methods were the least complex methods to use, but are restricted in their appropriateness. The two-stage MCS and quadrature-based methods are complex and time consuming. Thus, where appropriate, EVPPI should be estimated using either the single MCS or UNLI method. However, where neither of these methods is appropriate, either of the two-stage MCS and quadrature methods should be used.
authors.affiliation: Department of Epidemiology and Community Medicine, University of Ottawa, 451 Smyth Road, Ottawa, ON, Canada, K1H 8M5. dcoyle@uottawa.ca
authors.forename: Doug
authors.initials: D
authors.lastname: Coyle
authors.forename: Jeremy
authors.initials: J
authors.lastname: Oakley
mesh_terms: D002170:Canada; D003657:Decision Making; D017048:Health Care Costs; D006801:Humans; D008390:Markov Chains; D018803:Models, Economic; D009010:Monte Carlo Method; D009864:Ontario; D010300:Parkinson Disease / Q000191:economics; D010865:Pilot Projects; D013223:Statistics as Topic / Q000379:methods*; D006113:United Kingdom