Estimating the expected value of partial perfect information: a review of methods.

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.