TY - JOUR
T1 - Computing the expected value of sample information efficiently: Expertise and skills required for four model-based methods
AU - Kunst, Natalia R.
AU - Wilson, Ed
AU - Alarid-Escudero, Fernando
AU - Baio, Gianluca
AU - Brennan, Alan
AU - Fairley, Michael
AU - Glynn, David
AU - Goldhaber-Fiebert, Jeremy D.
AU - Jackson, Chris
AU - Jalal, Hawre
AU - Menzies, Nicolas A.
AU - Strong, Mark
AU - Thom, Howard
AU - Heath, Anna
PY - 2020/6
Y1 - 2020/6
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85085290622&partnerID=8YFLogxK
U2 - 10.1016/j.jval.2020.02.010
DO - 10.1016/j.jval.2020.02.010
M3 - Article
SN - 1098-3015
VL - 23
SP - 734
EP - 742
JO - Value in Health
JF - Value in Health
IS - 6
ER -