PMID- 17638032
OWN - NLM
STAT- MEDLINE
DCOM- 20081231
LR  - 20161124
IS  - 1618-7598 (Print)
IS  - 1618-7598 (Linking)
VI  - 9
IP  - 3
DP  - 2008 Aug
TI  - Estimating the expected value of partial perfect information: a review of 
      methods.
PG  - 251-9
AB  - 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.
FAU - Coyle, Doug
AU  - Coyle D
AD  - Department of Epidemiology and Community Medicine, University of Ottawa, 451 
      Smyth Road, Ottawa, ON, Canada, K1H 8M5. dcoyle@uottawa.ca
FAU - Oakley, Jeremy
AU  - Oakley J
LA  - eng
PT  - Comparative Study
PT  - Journal Article
DEP - 20070719
PL  - Germany
TA  - Eur J Health Econ
JT  - The European journal of health economics : HEPAC : health economics in prevention 
      and care
JID - 101134867
SB  - IM
MH  - Canada
MH  - *Decision Making
MH  - Health Care Costs
MH  - Humans
MH  - Markov Chains
MH  - *Models, Economic
MH  - Monte Carlo Method
MH  - Ontario
MH  - Parkinson Disease/economics
MH  - Pilot Projects
MH  - Statistics as Topic/*methods
MH  - United Kingdom
EDAT- 2007/07/20 09:00
MHDA- 2009/01/01 09:00
CRDT- 2007/07/20 09:00
PHST- 2005/12/18 00:00 [received]
PHST- 2007/06/11 00:00 [accepted]
PHST- 2007/07/20 09:00 [pubmed]
PHST- 2009/01/01 09:00 [medline]
PHST- 2007/07/20 09:00 [entrez]
AID - 10.1007/s10198-007-0069-y [doi]
PST - ppublish
SO  - Eur J Health Econ. 2008 Aug;9(3):251-9. doi: 10.1007/s10198-007-0069-y. Epub 2007 
      Jul 19.