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Eur J Health Econ. 2018 Jul;19(6):861-870. doi: 10.1007/s10198-017-0928-0. Epub 2017 Sep 4.

A comparison of the responsiveness of EQ-5D-5L and the QOLIE-31P and mapping of QOLIE-31P to EQ-5D-5L in epilepsy.

Author information

1
Department of Health Services Research, CAPHRI School of Public Health and Primary Care, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, The Netherlands. b.wijnen@maastrichtuniversity.nl.
2
Department of Research and Development, Epilepsy Centre Kempenhaeghe, Heeze, The Netherlands. b.wijnen@maastrichtuniversity.nl.
3
, Duboisdomein 30, 6229 GT, Maastricht, The Netherlands. b.wijnen@maastrichtuniversity.nl.
4
King's Health Economics (KHE), Institute of Psychiatry, Psychology and Neuroscience at King's College London, London, UK.
5
Department of Research and Development, Epilepsy Centre Kempenhaeghe, Heeze, The Netherlands.
6
Department of Neurology, Academic Centre for Epileptology, Epilepsy Centre Kempenhaeghe and Maastricht University Medical Centre, Maastricht, The Netherlands.
7
School of Mental Health and Neuroscience, Maastricht University Medical Center, Maastricht, The Netherlands.
8
School of Health Professions Education, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands.
9
Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology and Neuroscience at King's College London, London, UK.
10
Department of Health Services Research, CAPHRI School of Public Health and Primary Care, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, The Netherlands.
11
Trimbos Institute, Netherlands Institute of Mental Health and Addiction, Utrecht, The Netherlands.

Abstract

OBJECTIVE:

To investigate the responsiveness of and correlation between the EQ-5D-5L and the QOLIE-31P in patients with epilepsy, and develop a mapping function to predict EQ-5D-5L values based on the QOLIE-31P for use in economic evaluations.

METHODS:

The dataset was derived from two clinical trials, the ZMILE study in the Netherlands and the SMILE study in the UK. In both studies, patients' quality of life using the EQ-5D-5L and QOLIE-31P was measured at baseline and 12 months follow-up. Spearman's correlations, effect sizes (EF) and standardized response means (SRM) were calculated for both the EQ-5D-5L and QOLIE-31P domains and sub scores. Mapping functions were derived using ordinary least square (OLS) and censored least absolute deviations models.

RESULTS:

A total of 509 patients were included in this study. Low to moderately strong significant correlations were found between both instruments. The EQ-5D-5L showed high ceiling effects and small EFs and SRMs, whereas the QOLIE-31P did not show ceiling effects and also showed small to moderate EFs and SRMs. Results of the different mapping functions indicate that the highest adjusted R 2 we were able to regress was 0.265 using an OLS model with squared terms, leading to a mean absolute error of 0.103.

CONCLUSIONS:

Results presented in this study emphasize the shortcomings of the EQ-5D-5L in epilepsy and the importance of the development of condition-specific preference-based instruments which can be used within the QALY framework. In addition, the usefulness of the constructed mapping function in economic evaluations is questionable.

KEYWORDS:

Epilepsy; Mapping; Quality of life; Responsiveness

PMID:
28871490
PMCID:
PMC6008365
DOI:
10.1007/s10198-017-0928-0
[Indexed for MEDLINE]
Free PMC Article

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