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Med Decis Making. 2012 Sep-Oct;32(5):722-32.

Model parameter estimation and uncertainty analysis: a report of the ISPOR-SMDM Modeling Good Research Practices Task Force Working Group-6.

Author information

1
Institute of Health & Wellbeing, University of Glasgow, Glasgow, UK (AHB, EALF)
2
Harvard School of Public Health, Boston, Massachusetts, USA (MCW)
3
School of Population Health and Clinical Practice, University of Adelaide, SA, Australia (JK)
4
Centre for Health Economics, University of York, York, UK (MJS)
5
Yale School of Medicine and Yale School of Management, New Haven, Connecticut, USA (ADP)

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.

PMID:
22990087
DOI:
10.1177/0272989X12458348
[Indexed for MEDLINE]

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