TY - JOUR
T1 - Model parameter estimation and uncertainty
T2 - a report of the ISPOR-SMDM modeling good research practices task force-6
AU - ISPOR-SMDM Modeling Good Research Practices Task Force
AU - Briggs, Andrew H.
AU - Weinstein, Milton C.
AU - Fenwick, Elisabeth A.L.
AU - Karnon, Jonathan
AU - Sculpher, Mark J
AU - Paltiel, A. David
PY - 2012/9
Y1 - 2012/9
N2 - 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, in terms of both 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.
AB - 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, in terms of both 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.
KW - Best practices
KW - heterogeneity
KW - sensitivity analysis
KW - uncertainty analysis
KW - best practices
KW - value of information
UR - http://www.scopus.com/inward/record.url?scp=84866423931&partnerID=8YFLogxK
U2 - 10.1016/j.jval.2012.04.014
DO - 10.1016/j.jval.2012.04.014
M3 - Article
SN - 1098-3015
VL - 15
SP - 835
EP - 842
JO - Value in Health
JF - Value in Health
IS - 6
ER -