Minimal modeling approaches to value of information analysis for health research

Abstract

Value of information (VOI) techniques can provide estimates of the expected benefits from clinical research studies that 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, but the complexity of constructing such models can pose barriers to some practical applications 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 article 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 2 new clinical applications to which the approach appears well suited because clinical trials with comprehensive outcomes provide preliminary estimates of the uncertainty in outcomes. The authors conclude that minimal modeling approaches to VOI can be readily applied in some instances to estimate the expected benefits of clinical research.