Computing the Expected Value of Sample Information Efficiently: Expertise and Skills Required for Four Model-Based Methods

Natalia R. Kunst, Ed Wilson, Fernando Alarid-Escudero, Gianluca Baio, Alan Brennan, Michael Fairley, David Glynn, Jeremy D. Goldhaber-Fiebert, Chris Jackson, Hawre Jalal, Nicolas A. Menzies, Mark Strong, Howard Thom, Anna Heath

Research output: Contribution to journalArticle

9 Citations (Scopus)
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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.

Original languageEnglish
Pages (from-to)734-742
Number of pages9
JournalValue in Health
Volume23
Issue number6
Early online date27 May 2020
DOIs
Publication statusPublished - Jun 2020

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