| An Efficient Method for Computing Single-Parameter Partial Expected Value of Perfect Information. | |
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MedLine Citation:
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PMID: 23275450 Owner: NLM Status: Publisher |
Abstract/OtherAbstract:
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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. |
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Authors:
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Mark Strong; Jeremy E Oakley |
Publication Detail:
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Type: JOURNAL ARTICLE Date: 2012-12-28 |
Journal Detail:
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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:
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Created Date: 2012-12-31 Completed Date: - Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 8109073 Medline TA: Med Decis Making Country: - |
Other Details:
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Languages: ENG Pagination: - Citation Subset: - |
Affiliation:
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School of Health and Related Research (ScHARR), University of Sheffield, UK (MS). |
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From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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