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Model Parameter Estimation and Uncertainty Analysis: A Report of the ISPOR-SMDM Modeling Task Force-6

2012

This paper discusses methods for the reporting of uncertainty, both in terms of deterministic sensitivity analysis techniques and probabilistic methods. 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. The article describes the process of estimating model inputs, whether these are point estimates or distributions. It also explores the link between parameter uncertainty, decision uncertainty, and value-of-information analysis.

Recommendations are provided on best choices for reporting, including expected value of perfect information, which is argued to be the most appropriate presentational technique, alongside cost-effectiveness acceptability curves, for representing decision uncertainty from probabilistic analysis.

This paper is one of a 7-part series of articles on modeling good research practices based on a collaboration between the International Society for Pharmacoeconomics and Outcomes Research (ISPOR) and the Society for Medical Decision Making (SMDM).

The other articles include:

 

Source:

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 in Health 2012; 15: 835-842. https://doi.org/10.1177/0272989X12458348