Please use this identifier to cite or link to this item: http://hdl.handle.net/2440/91864
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Type: Journal article
Title: Model parameter estimation and uncertainty analysis: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force Working Group-6
Author: Briggs, A.
Weinstein, M.
Fenwick, E.
Karnon, J.
Sculpher, M.
Paltiel, A.
Citation: Medical Decision Making, 2012; 32(5):722-732
Publisher: SAGE Publications
Issue Date: 2012
ISSN: 0272-989X
1552-681X
Statement of
Responsibility: 
Andrew H. Briggs, Milton C. Weinstein, Elisabeth A. L. Fenwick, Jonathan Karnon, Mark J. Sculpher, A. David Paltiel on behalf of the ISPOR-SMDM Modeling Good Research Practices Task Force
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.
Keywords: uncertainty analysis; sensitivity analysis; heterogeneity; value of information; guidelines
Rights: Copyright status unknown
RMID: 0030022772
DOI: 10.1177/0272989X12458348
Appears in Collections:Public Health publications

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