Computing the expected value of sample information efficiently: Expertise and skills required for four model-based methods

Kunst, Natalia R., Wilson, Ed ORCID: https://orcid.org/0000-0002-8369-1577, Alarid-Escudero, Fernando, Baio, Gianluca, Brennan, Alan, Fairley, Michael, Glynn, David, Goldhaber-Fiebert, Jeremy D., Jackson, Chris, Jalal, Hawre, Menzies, Nicolas A., Strong, Mark, Thom, Howard and Heath, Anna (2020) Computing the expected value of sample information efficiently: Expertise and skills required for four model-based methods. Value in Health, 23 (6). pp. 734-742. ISSN 1098-3015

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Abstract

Value of information (VOI) analyses can help policy makers make informed decisions about whether to conduct and how to design future studies. Historically a computationally expensive method to compute the expected value of sample information (EVSI) restricted the use of VOI to simple decision models and study designs. Recently, 4 EVSI approximation methods have made such analyses more feasible and accessible. Members of the Collaborative Network for Value of Information (ConVOI) compared the inputs, the analyst's expertise and skills, and the software required for the 4 recently developed EVSI approximation methods. Our report provides practical guidance and recommendations to help inform the choice between the 4 efficient EVSI estimation methods. More specifically, this report provides: (1) a step-by-step guide to the methods’ use, (2) the expertise and skills required to implement the methods, and (3) method recommendations based on the features of decision-analytic problems.

Item Type: Article
Uncontrolled Keywords: health policy,public health, environmental and occupational health,sdg 3 - good health and well-being ,/dk/atira/pure/subjectarea/asjc/2700/2719
Faculty \ School: Faculty of Medicine and Health Sciences > Norwich Medical School
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Depositing User: LivePure Connector
Date Deposited: 03 Jun 2020 23:57
Last Modified: 22 Oct 2022 05:19
URI: https://ueaeprints.uea.ac.uk/id/eprint/75455
DOI: 10.1016/j.jval.2020.02.010

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