Mapping of the DLQI scores to EQ-5D utility values using ordinal logistic regression

Ali, Faraz Mahmood, Kay, Richard, Finlay, Andrew Yule ORCID: https://orcid.org/0000-0003-2143-1646, Piguet, Vincent, Kupfer, Joerg, Dalgard, Florence and Salek, M. Sam 2017. Mapping of the DLQI scores to EQ-5D utility values using ordinal logistic regression. Quality of Life Research 26 , pp. 3025-3034. 10.1007/s11136-017-1607-4

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Abstract

Purpose 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 specialtyspecific 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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	Medicine Pharmacy
Publisher:	Springer Verlag
ISSN:	0962-9343
Date of First Compliant Deposit:	12 July 2017
Date of Acceptance:	27 May 2017
Last Modified:	03 May 2023 11:38
URI:	https://orca.cardiff.ac.uk/id/eprint/102356

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