Value of Information: Sensitivity Analysis and Research Design in Bayesian Evidence Synthesis.


Type
Article
Change log
Authors
Jackson, Christopher  ORCID logo  https://orcid.org/0000-0002-6656-8913
Conti, Stefano 
De Angelis, Daniela  ORCID logo  https://orcid.org/0000-0001-6619-6112
Abstract

Suppose we have a Bayesian model that combines evidence from several different sources. We want to know which model parameters most affect the estimate or decision from the model, or which of the parameter uncertainties drive the decision uncertainty. Furthermore, we want to prioritize what further data should be collected. These questions can be addressed by Value of Information (VoI) analysis, in which we estimate expected reductions in loss from learning specific parameters or collecting data of a given design. We describe the theory and practice of VoI for Bayesian evidence synthesis, using and extending ideas from health economics, computer modeling and Bayesian design. The methods are general to a range of decision problems including point estimation and choices between discrete actions. We apply them to a model for estimating prevalence of HIV infection, combining indirect information from surveys, registers, and expert beliefs. This analysis shows which parameters contribute most of the uncertainty about each prevalence estimate, and the expected improvements in precision from specific amounts of additional data. These benefits can be traded with the costs of sampling to determine an optimal sample size. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

Description
Keywords
Decision theory, Research prioritization, Uncertainty
Journal Title
J Am Stat Assoc
Conference Name
Journal ISSN
0162-1459
1537-274X
Volume Title
114
Publisher
Informa UK Limited