期刊
JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES A-STATISTICS IN SOCIETY
卷 172, 期 -, 页码 383-404出版社
WILEY
DOI: 10.1111/j.1467-985X.2008.00573.x
关键词
Akaike's information criterion; Bayesian information criterion; Health economics; Model averaging; Model uncertainty
资金
- UK Medical Research Council [U.1052.00.008, U.1052.00.001]
- British Heart Foundation [RG/08/014/24067] Funding Source: researchfish
- Medical Research Council [MC_U105232027] Funding Source: researchfish
- MRC [MC_U105232027] Funding Source: UKRI
Health economic decision models are subject to considerable uncertainty, much of which arises from choices between several plausible model structures, e.g. choices of covariates in a regression model. Such structural uncertainty is rarely accounted for formally in decision models but can be addressed by model averaging. We discuss the most common methods of averaging models and the principles underlying them. We apply them to a comparison of two surgical techniques for repairing abdominal aortic aneurysms. In model averaging, competing models are usually either weighted by using an asymptotically consistent model assessment criterion, such as the Bayesian information criterion, or a measure of predictive ability, such as Akaike's information criterion. We argue that the predictive approach is more suitable when modelling the complex underlying processes of interest in health economics, such as individual disease progression and response to treatment.
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