Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/62009
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dc.contributor.authorEckermann, S.en
dc.contributor.authorKarnon, J.en
dc.contributor.authorWillan, A.en
dc.date.issued2010en
dc.identifier.citationPharmacoeconomics, 2010; 28(9):699-709en
dc.identifier.issn1170-7690en
dc.identifier.issn1179-2027en
dc.identifier.urihttp://hdl.handle.net/2440/62009-
dc.description.abstractValue of information (VOI) methods have been proposed as a systematic approach to inform optimal research design and prioritization. Four related questions arise that VOI methods could address. (i) Is further research for a health technology assessment (HTA) potentially worthwhile? (ii) Is the cost of a given research design less than its expected value? (iii) What is the optimal research design for an HTA? (iv) How can research funding be best prioritized across alternative HTAs? Following Occam's razor, we consider the usefulness of VOI methods in informing questions 1–4 relative to their simplicity of use. Expected value of perfect information (EVPI) with current information, while simple to calculate, is shown to provide neither a necessary nor a sufficient condition to address question 1, given that what EVPI needs to exceed varies with the cost of research design, which can vary from very large down to negligible. Hence, for any given HTA, EVPI does not discriminate, as it can be large and further research not worthwhile or small and further research worthwhile. In contrast, each of questions 1–4 are shown to be fully addressed (necessary and sufficient) where VOI methods are applied to maximize expected value of sample information (EVSI) minus expected costs across designs. In comparing complexity in use of VOI methods, applying the central limit theorem (CLT) simplifies analysis to enable easy estimation of EVSI and optimal overall research design, and has been shown to outperform bootstrapping, particularly with small samples. Consequently, VOI methods applying the CLT to inform optimal overall research design satisfy Occam's razor in both improving decision making and reducing complexity. Furthermore, they enable consideration of relevant decision contexts, including option value and opportunity cost of delay, time, imperfect implementation and optimal design across jurisdictions. More complex VOI methods such as bootstrapping of the expected value of partial EVPI may have potential value in refining overall research design. However, Occam's razor must be seriously considered in application of these VOI methods, given their increased complexity and current limitations in informing decision making, with restriction to EVPI rather than EVSI and not allowing for important decision-making contexts. Initial use of CLT methods to focus these more complex partial VOI methods towards where they may be useful in refining optimal overall trial design is suggested. Integrating CLT methods with such partial VOI methods to allow estimation of partial EVSI is suggested in future research to add value to the current VOI toolkit.en
dc.description.statementofresponsibilitySimon Eckermann, Jon Karnon and Andrew R. Willanen
dc.language.isoenen
dc.publisherAdis International Ltden
dc.rights© 2010 Adis Data Information BV. All rights reserved.en
dc.source.urihttp://adisonline.com/pharmacoeconomics/Fulltext/2010/28090/The_Value_of_Value_of_Information__Best_Informing.1.aspxen
dc.subjectHumans; Models, Economic; Epidemiologic Research Design; Decision Support Techniques; Research Design; Biomedical Technology; Cost-Benefit Analysis; Technology Assessment, Biomedical; Clinical Trials as Topicen
dc.titleThe value of value of information: best informing research design and prioritization using current methodsen
dc.typeJournal articleen
dc.identifier.rmid0020100910en
dc.identifier.doi10.2165/11537370-000000000-00000en
dc.identifier.pubid33274-
pubs.library.collectionPublic Health publicationsen
pubs.verification-statusVerifieden
pubs.publication-statusPublisheden
dc.identifier.orcidKarnon, J. [0000-0003-3220-2099]en
Appears in Collections:Public Health publications

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