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《傻瓜式健康经济诊断决策树模型:图解入门》

Health Economic Decision Tree Models of Diagnostics for Dummies: A Pictorial Primer.

作者信息

Rautenberg Tamlyn, Gerritsen Annette, Downes Martin

机构信息

Centre for Applied Health Economics, Griffith University, Nathan 4111, Australia.

EpiResult, Consultancy, Pietermaritzburg 3201, South Africa.

出版信息

Diagnostics (Basel). 2020 Mar 14;10(3):158. doi: 10.3390/diagnostics10030158.

Abstract

Health economics is a discipline of economics applied to health care. One method used in health economics is decision tree modelling, which extrapolates the cost and effectiveness of competing interventions over time. Such decision tree models are the basis of reimbursement decisions in countries using health technology assessment for decision making. In many instances, these competing interventions are diagnostic technologies. Despite a wealth of excellent resources describing the decision analysis of diagnostics, two critical errors persist: not including diagnostic test accuracy in the structure of decision trees and treating sequential diagnostics as independent. These errors have consequences for the accuracy of model results, and thereby impact on decision making. This paper sets out to overcome these errors using color to link fundamental epidemiological calculations to decision tree models in a visually and intuitively appealing pictorial format. The paper is a must-read for modelers developing decision trees in the area of diagnostics for the first time and decision makers reviewing diagnostic reimbursement models.

摘要

卫生经济学是一门应用于医疗保健领域的经济学学科。卫生经济学中使用的一种方法是决策树建模,它可以推断出随着时间推移相互竞争的干预措施的成本和效果。这种决策树模型是在使用卫生技术评估进行决策的国家中报销决策的基础。在许多情况下,这些相互竞争的干预措施是诊断技术。尽管有大量优秀资源描述诊断的决策分析,但仍存在两个关键错误:在决策树结构中未纳入诊断测试准确性,以及将序贯诊断视为独立的。这些错误会影响模型结果的准确性,进而影响决策。本文旨在通过以视觉上和直观上吸引人的图形格式使用颜色将基本的流行病学计算与决策树模型联系起来,以克服这些错误。对于首次在诊断领域开发决策树的建模人员以及审查诊断报销模型的决策者来说,本文是必读之作。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/43dc/7151142/5844e8a1929a/diagnostics-10-00158-g001.jpg

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