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AuthorAlam, M Fasihul
AuthorBriggs, Andrew
Available date2019-10-13T07:13:39Z
Publication Date2019-09-01
Publication NameExpert Review of Pharmacoeconomics & Outcomes Researchen_US
CitationM Fasihul Alam & Andrew Briggs (2019): Artificial neural network metamodel for sensitivity analysis in a total hip replacement health economic model, Expert Review of Pharmacoeconomics & Outcomes Research, DOI: 10.1080/14737167.2019.1665512
Abstract: Metamodels have been used to approximate complex simulations and have many applications with sensitivity analysis, optimization, etc. However, their use in health economics is very limited. Application of artificial neural network (ANN) with a health economic model has never been investigated. The study intends to introduce ANN as a metamodeling method to conduct sensitivity analysis in a total hip replacement decision analytical model and compare its performance with two other counterparts. : First, a nonlinear factor screening method was adopted to screen out unimportant factors from the simulation. Second, an ANN was developed using the important variables to approximate the simulation. Performance of the ANN metamodel was then compared with its Gaussian Process (GP) and multiple linear regression (MLR) counterparts. : Out of 31, the factor screening method identified 12 important variables from the simulation. ANN metamodels showed best predictive capabilities in terms of performance measures (mean squared error of prediction, MSEP and mean absolute percentage deviation, MAPD) used for predicting both costs and quality-adjusted life years (QALYs) for two prostheses. : The study provides a methodological development in sensitivity analysis and demonstrates that an ANN metamodel is a potential approximation method for computationally expensive health economic simulations.
PublisherTaylor & Francis
Subjecthealth economic model
Subjectneural network
Subjectsensitivity analysis
TitleArtificial neural network metamodel for sensitivity analysis in a total hip replacement health economic model.
dc.identifier.essn 1744-8379

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