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dc.contributor.authorAli, Faraz Mahmood
dc.contributor.authorKay, Richard
dc.contributor.authorFinlay, Andrew Y.
dc.contributor.authorPiguet, Vincent
dc.contributor.authorKupfer, Joerg
dc.contributor.authorDalgard, Florence
dc.contributor.authorSalek, M. Sam
dc.date.accessioned2018-01-31T17:52:35Z
dc.date.available2018-01-31T17:52:35Z
dc.date.issued2017-11-30
dc.identifier.citationAli , F M , Kay , R , Finlay , A Y , Piguet , V , Kupfer , J , Dalgard , F & Salek , M S 2017 , ' Mapping of the DLQI scores to EQ-5D utility values using ordinal logistic regression ' , Quality of Life Research , vol. 26 , pp. 3025-3034 . https://doi.org/10.1007/s11136-017-1607-4
dc.identifier.issn0962-9343
dc.identifier.otherPURE: 12082605
dc.identifier.otherPURE UUID: 481f9f32-a0cd-4646-8a78-795260bf76da
dc.identifier.otherScopus: 85020636923
dc.identifier.urihttp://hdl.handle.net/2299/19700
dc.descriptionOpen Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://crea tivecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
dc.description.abstractPurpose: The Dermatology Life Quality Index (DLQI) and the European Quality of Life-5 Dimension (EQ-5D) are separate measures that may be used to gather health-related quality of life (HRQoL) information from patients. The EQ-5D is a generic measure from which health utility estimates can be derived, whereas the DLQI is a specialty-specific measure to assess HRQoL. To reduce the burden of multiple measures being administered and to enable a more disease-specific calculation of health utility estimates, we explored an established mathematical technique known as ordinal logistic regression (OLR) to develop an appropriate model to map DLQI data to EQ-5D-based health utility estimates. Methods: Retrospective data from 4010 patients were randomly divided five times into two groups for the derivation and testing of the mapping model. Split-half cross-validation was utilized resulting in a total of ten ordinal logistic regression models for each of the five EQ-5D dimensions against age, sex, and all ten items of the DLQI. Using Monte Carlo simulation, predicted health utility estimates were derived and compared against those observed. This method was repeated for both OLR and a previously tested mapping methodology based on linear regression. Results: The model was shown to be highly predictive and its repeated fitting demonstrated a stable model using OLR as well as linear regression. The mean differences between OLR-predicted health utility estimates and observed health utility estimates ranged from 0.0024 to 0.0239 across the ten modeling exercises, with an average overall difference of 0.0120 (a 1.6% underestimate, not of clinical importance). Conclusions: This modeling framework developed in this study will enable researchers to calculate EQ-5D health utility estimates from a specialty-specific study population, reducing patient and economic burden.en
dc.format.extent10
dc.language.isoeng
dc.relation.ispartofQuality of Life Research
dc.subjectDLQI
dc.subjectEQ-5D
dc.subjectMapping
dc.subjectOrdinal logistic regression
dc.subjectQuality of life
dc.subjectUtility values
dc.subjectPublic Health, Environmental and Occupational Health
dc.titleMapping of the DLQI scores to EQ-5D utility values using ordinal logistic regressionen
dc.contributor.institutionCentre for Health Services and Clinical Research
dc.contributor.institutionSchool of Life and Medical Sciences
dc.contributor.institutionUniversity of Hertfordshire
dc.description.statusPeer reviewed
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=85020636923&partnerID=8YFLogxK
rioxxterms.versionVoR
rioxxterms.versionofrecordhttps://doi.org/10.1007/s11136-017-1607-4
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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