Model Parameter Estimation and Uncertainty Analysis: A Report of the ISPOR-SMDM Modeling Good Research Practices Task Force Working Group–6 on Behalf of the ISPOR-SMDM Modeling Good Research Practices Task Force. Briggs, A. H, Weinstein, M. C, Fenwick, E. A L, Karnon, J., Sculpher, M. J, & Paltiel, A D. doi abstract bibtex A model's purpose is to inform medical decisions and health care resource allocation. Modelers employ quantita-tive 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 esti-mate for a specific outcome but also in the systematic examination and responsible reporting of uncertainty sur-rounding this outcome and the ultimate decision being ad-dressed. 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 param-eters is part of a single process and explores the link between parameter uncertainty through to decision uncer-tainty and the relationship to value-of-information analy-sis. The article also makes extensive recommendations around the reporting of uncertainty, both in terms of deter-ministic sensitivity analysis techniques and probabilistic methods. Expected value of perfect information is argued to be the most appropriate presentational technique, along-side cost-effectiveness acceptability curves, for representing decision uncertainty from probabilistic analysis.
@article{briggs_model_nodate,
title = {Model {Parameter} {Estimation} and {Uncertainty} {Analysis}: {A} {Report} of the {ISPOR}-{SMDM} {Modeling} {Good} {Research} {Practices} {Task} {Force} {Working} {Group}–6 on {Behalf} of the {ISPOR}-{SMDM} {Modeling} {Good} {Research} {Practices} {Task} {Force}},
doi = {10.1177/0272989X12458348},
abstract = {A model's purpose is to inform medical decisions and health care resource allocation. Modelers employ quantita-tive 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 esti-mate for a specific outcome but also in the systematic examination and responsible reporting of uncertainty sur-rounding this outcome and the ultimate decision being ad-dressed. 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 param-eters is part of a single process and explores the link between parameter uncertainty through to decision uncer-tainty and the relationship to value-of-information analy-sis. The article also makes extensive recommendations around the reporting of uncertainty, both in terms of deter-ministic sensitivity analysis techniques and probabilistic methods. Expected value of perfect information is argued to be the most appropriate presentational technique, along-side cost-effectiveness acceptability curves, for representing decision uncertainty from probabilistic analysis.},
author = {Briggs, Andrew H and Weinstein, Milton C and Fenwick, Elisabeth A L and Karnon, Jonathan and Sculpher, Mark J and Paltiel, A David},
}
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