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An Efficient Method for Computing Single-Parameter Partial Expected Value of Perfect Information.
MedLine Citation:
PMID:  23275450     Owner:  NLM     Status:  Publisher    
Abstract/OtherAbstract:
The value of learning an uncertain input in a decision model can be quantified by its partial expected value of perfect information (EVPI). This is commonly estimated via a 2-level nested Monte Carlo procedure in which the parameter of interest is sampled in an outer loop, and then conditional on this sampled value, the remaining parameters are sampled in an inner loop. This 2-level method can be difficult to implement if the joint distribution of the inner-loop parameters conditional on the parameter of interest is not easy to sample from. We present a simple alternative 1-level method for calculating partial EVPI for a single parameter that avoids the need to sample directly from the potentially problematic conditional distributions. We derive the sampling distribution of our estimator and show in a case study that it is both statistically and computationally more efficient than the 2-level method.
Authors:
Mark Strong; Jeremy E Oakley
Publication Detail:
Type:  JOURNAL ARTICLE     Date:  2012-12-28
Journal Detail:
Title:  Medical decision making : an international journal of the Society for Medical Decision Making     Volume:  -     ISSN:  1552-681X     ISO Abbreviation:  Med Decis Making     Publication Date:  2012 Dec 
Date Detail:
Created Date:  2012-12-31     Completed Date:  -     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  8109073     Medline TA:  Med Decis Making     Country:  -    
Other Details:
Languages:  ENG     Pagination:  -     Citation Subset:  -    
Affiliation:
School of Health and Related Research (ScHARR), University of Sheffield, UK (MS).
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