Published May 2012
Briggs AH, Weinstein MC, Fenwick E, et al. Model parameter estimation and uncertainty analysis: a report of the ISPOR-SMDM modeling good research practices task force-6. Value Health. 2012;15(5):835-842.
A model’s purpose is to inform medical decisions and health care resource
allocation. Modelers employ quantitative methods to structure
the clinical, epidemiological, and economic evidence base and gain
qualitative insight to assist decision makers in making better decisions.
From a policy perspective, the value of a model-based analysis
lies not simply in its ability to generate a precise point estimate for a
specific outcome but also in the systematic examination and responsible
reporting of uncertainty surrounding this outcome and the ultimate
decision being addressed.
Different concepts relating to uncertainty in decision modeling are explored. Stochastic (first-order) uncertainty is distinguished from both parameter (second-order) uncertainty and from heterogeneity, with structural uncertainty relating to the model itself forming another level of uncertainty to consider. The article argues that the estimation of point estimates and uncertainty in parameters is part of a single process and explores the link between parameter uncertainty through to decision uncertainty and the relationship to value of information analysis.
The article also makes extensive recommendations around the reporting of uncertainty, in terms of both deterministic sensitivity analysis techniques and probabilistic methods. Expected value of perfect information is argued to be the most appropriate presentational technique, alongside cost-effectiveness acceptability curves, for representing decision uncertainty from probabilistic analysis.
Keywords: best practices, heterogeneity, sensitivity analysis, uncertainty analysis, value of information.
Copyright © 2017, International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc.