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

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 us...

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Main Authors: Tamlyn Rautenberg, Annette Gerritsen, Martin Downes
Format: Article
Language:English
Published: MDPI AG 2020-03-01
Series:Diagnostics
Subjects:
Online Access:https://www.mdpi.com/2075-4418/10/3/158
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spelling doaj-599d910022e342dc860e42a4e5c396b62020-11-25T02:10:42ZengMDPI AGDiagnostics2075-44182020-03-0110315810.3390/diagnostics10030158diagnostics10030158Health Economic Decision Tree Models of Diagnostics for Dummies: A Pictorial PrimerTamlyn Rautenberg0Annette Gerritsen1Martin Downes2Centre for Applied Health Economics, Griffith University, Nathan 4111, AustraliaEpiResult, Consultancy, Pietermaritzburg 3201, South AfricaCentre for Applied Health Economics, Griffith University, Nathan 4111, AustraliaHealth 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.https://www.mdpi.com/2075-4418/10/3/158decision analysishealth economic modellingdiagnostic test
collection DOAJ
language English
format Article
sources DOAJ
author Tamlyn Rautenberg
Annette Gerritsen
Martin Downes
spellingShingle Tamlyn Rautenberg
Annette Gerritsen
Martin Downes
Health Economic Decision Tree Models of Diagnostics for Dummies: A Pictorial Primer
Diagnostics
decision analysis
health economic modelling
diagnostic test
author_facet Tamlyn Rautenberg
Annette Gerritsen
Martin Downes
author_sort Tamlyn Rautenberg
title Health Economic Decision Tree Models of Diagnostics for Dummies: A Pictorial Primer
title_short Health Economic Decision Tree Models of Diagnostics for Dummies: A Pictorial Primer
title_full Health Economic Decision Tree Models of Diagnostics for Dummies: A Pictorial Primer
title_fullStr Health Economic Decision Tree Models of Diagnostics for Dummies: A Pictorial Primer
title_full_unstemmed Health Economic Decision Tree Models of Diagnostics for Dummies: A Pictorial Primer
title_sort health economic decision tree models of diagnostics for dummies: a pictorial primer
publisher MDPI AG
series Diagnostics
issn 2075-4418
publishDate 2020-03-01
description 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.
topic decision analysis
health economic modelling
diagnostic test
url https://www.mdpi.com/2075-4418/10/3/158
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AT annettegerritsen healtheconomicdecisiontreemodelsofdiagnosticsfordummiesapictorialprimer
AT martindownes healtheconomicdecisiontreemodelsofdiagnosticsfordummiesapictorialprimer
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