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dc.contributor.authorCorro Ramos, Isaac
dc.contributor.authorvan Voorn, George A K
dc.contributor.authorVemer, Pepijn
dc.contributor.authorFeenstra, Talitha L
dc.contributor.authorAl, Maiwenn J
dc.date.accessioned2018-02-07T08:54:50Z
dc.date.available2018-02-07T08:54:50Z
dc.date.issued2017
dc.identifier.citationA New Statistical Method to Determine the Degree of Validity of Health Economic Model Outcomes against Empirical Data. 2017, 20 (8):1041-1047 Value Healthen
dc.identifier.issn1524-4733
dc.identifier.pmid28964435
dc.identifier.doi10.1016/j.jval.2017.04.016
dc.identifier.urihttp://hdl.handle.net/10029/621370
dc.description.abstractThe validation of health economic (HE) model outcomes against empirical data is of key importance. Although statistical testing seems applicable, guidelines for the validation of HE models lack guidance on statistical validation, and actual validation efforts often present subjective judgment of graphs and point estimates.
dc.language.isoenen
dc.rightsinfo:eu-repo/semantics/closedAccessen
dc.subject.meshBayes Theorem
dc.subject.meshData Interpretation, Statistical
dc.subject.meshDecision Making
dc.subject.meshDiabetes Complications
dc.subject.meshGuidelines as Topic
dc.subject.meshHumans
dc.subject.meshKidney Failure, Chronic
dc.subject.meshModels, Economic
dc.subject.meshProbability
dc.subject.meshRenal Dialysis
dc.subject.meshValidation Studies as Topic
dc.titleA New Statistical Method to Determine the Degree of Validity of Health Economic Model Outcomes against Empirical Data.en
dc.typeArticleen
dc.identifier.journalValue Health 2017; 20(8):1041-7en
html.description.abstractThe validation of health economic (HE) model outcomes against empirical data is of key importance. Although statistical testing seems applicable, guidelines for the validation of HE models lack guidance on statistical validation, and actual validation efforts often present subjective judgment of graphs and point estimates.


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