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Minimal Modeling Approaches to Value of Information Analysis for Health Research

Research Report

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This report is from AHRQ's Future Research Needs – Methods Research Series.

Structured Abstract

Value of information (VOI) techniques can provide estimates of the expected benefits from clinical research studies. These VOI estimates can inform decisions about the design and priority of those studies. Most VOI studies use decision analytic models to characterize the uncertainty of the effects of interventions on health outcomes. For some potential applications of VOI, the complexity of constructing such models poses barriers to practical application of VOI. However, because some clinical studies can directly characterize uncertainty in health outcomes, it may sometimes be possible to perform VOI analysis with only minimal modeling. This paper (1) develops a framework to define and classify minimal modeling approaches to VOI; (2) reviews existing VOI studies that apply minimal modeling approaches; and (3) illustrates and discusses the application of the minimal modeling to two new clinical applications to which the approach appears well suited because clinical trials with comprehensive outcomes provide preliminary estimates of the uncertainty in outcomes. We conclude that minimal modeling approaches to VOI can be readily applied to in some instances to develop estimates of the expected benefits of clinical research.

Journal Publications

Meltzer DO, Hoomans T, Chung JW, et al. Minimal modeling approaches to value of information analysis for health research. Med Decis Making 2011 Jun 28 [Epub ahead of print]. PMID: 21712493.