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10-06-2017 | Uitgave 11/2017 Open Access

Quality of Life Research 11/2017

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

Tijdschrift:
Quality of Life Research > Uitgave 11/2017
Auteurs:
Faraz Mahmood Ali, Richard Kay, Andrew Y. Finlay, Vincent Piguet, Joerg Kupfer, Florence Dalgard, M. Sam Salek
Belangrijke opmerkingen

Electronic supplementary material

The online version of this article (doi:10.​1007/​s11136-017-1607-4) contains supplementary material, which is available to authorized users.

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 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.

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