Model parameter estimation and uncertainty analysis: a report of the ISPOR-SMDM modeling good research practices task force working group - 6

Briggs, A.H. , Weinstein, M.C., Fenwick, E.A.L., Karnon, J., Sculpher, M.J. and Paltiel, A.D. (2012) Model parameter estimation and uncertainty analysis: a report of the ISPOR-SMDM modeling good research practices task force working group - 6. Medical Decision Making, 32(5), pp. 722-732. (doi: 10.1177/0272989X12458348)

Abstract

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, both in terms of 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.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Fenwick, Professor Elisabeth and Briggs, Professor Andrew
• Authors: Briggs, A.H., Weinstein, M.C., Fenwick, E.A.L., Karnon, J., Sculpher, M.J., and Paltiel, A.D.
• College/School: College of Medical Veterinary and Life Sciences > School of Health & Wellbeing > Health Economics and Health Technology Assessment
• Journal Name: Medical Decision Making
• Publisher: Sage
• ISSN: 0272-989X
• Copyright Holders: Copyright © 2012 The Authors
• First Published: First published in Medical Decision Making 32(5):722-732
• Publisher Policy: Reproduced under a Creative Commons License