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

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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.

Electronic supplementary material

The online version of this article (doi:10.1007/s10198-017-0928-0) contains supplementary material, which is available to authorized users.

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Most cited references 24

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A review of studies mapping (or cross walking) non-preference based measures of health to generic preference-based measures.

John Brazier, Yaling Yang, Aki Tsuchiya … (2010)

Clinical studies use a wide variety of health status measures to measure health related quality of life, many of which cannot be used in cost-effectiveness analysis using cost per quality adjusted life year (QALY). Mapping is one solution that is gaining popularity as it enables health state utility values to be predicted for use in cost per QALY analysis when no preference-based measure has been included in the study. This paper presents a systematic review of current practice in mapping between non-preference based measures and generic preference-based measures, addressing feasibility and validity, circumstances under which it should be considered and lessons for future mapping studies. This review found 30 studies reporting 119 different models. Performance of the mappings functions in terms of goodness-of-fit and prediction was variable and unable to be generalised across instruments. Where generic measures are not regarded as appropriate for a condition, mapping does not solve this problem. Most testing in the literature occurs at the individual level yet the main purpose of these functions is to predict mean values for subgroups of patients, hence more testing is required.

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Comparative responsiveness of generic and specific quality-of-life instruments.

Suzan Matijevic, Casey Sidwell, Bruce Weaver … (2002)

We assessed the relative responsiveness of generic and specific quality of life instruments in 43 randomized controlled trials that compared head-to-head 31 generic and 84 specific instruments. Using weighted effect size as the metric of responsiveness, we assessed the impact of instrument type, disease category, and magnitude of underlying therapeutic effect on responsiveness, and assessed the responsiveness of specific instruments relative to the corresponding domains of generic measures. In studies with a nonzero therapeutic effect, specific instruments (mean = 0.57) were significantly more responsive than generic instruments (mean = 0.39, P =.01), and than the corresponding domains of generic instruments (mean = 0.40, P =.03). Studies with low, medium, and high therapeutic effects showed a corresponding gradation in responsiveness differences between specific and generic instruments. We conclude that, overall, specific instruments are more responsive than generic tools, and that investigators may come to misleading conclusions about relative instrument responsiveness if they include studies in which the magnitude of the underlying therapeutic effect is zero.

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Mapping to obtain EQ-5D utility values for use in NICE health technology assessments.

Louise Longworth, Donna Rowen (2015)

Quality-adjusted life-years (QALYs) are widely used as an outcome for the economic evaluation of health interventions. However, preference-based measures used to obtain health-related utility values to produce QALY estimates are not always included in key clinical studies. Furthermore, organizations responsible for reviewing or producing health technology assessments (HTAs) may have preferred instruments for obtaining utility estimates for QALY calculations. Where data using a preference-based measure or the preferred instrument have not been collected, it may be possible to "map" or "crosswalk" from other measures of health outcomes. The aims of this study were 1) to provide an overview of how mapping is currently used as reported in the published literature and in an HTA policy-making context, specifically at the National Institute for Health and Clinical Excellence in the United Kingdom, and 2) to comment on best current practice on the use of mapping for HTA more generally. The review of the National Institute for Health and Clinical Excellence guidance found that mapping has been used since first established but that reporting of the models used to map has been poor. Recommendations for mapping in HTA include an explicit consideration of the generalizability of the mapping function to the target sample, reporting of standard econometric and statistical tests including the degree of error in the mapping model across subsets of the range of utility values, and validation of the model(s). Mapping can provide a route for linking outcomes data collected in a trial or observational study to the specific preferred instrument for obtaining utility values. In most cases, however, it is still advantageous to directly collect data by using the preferred utility-based instrument and mapping should usually be viewed as a "second-best" solution. Copyright © 2013 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

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Author and article information

Contributors

Ben F. M. Wijnen:

ORCID: http://orcid.org/0000-0001-7993-1905

+31 43 38 82294 , b.wijnen@maastrichtuniversity.nl

Journal

Journal ID (nlm-ta): Eur J Health Econ

Journal ID (iso-abbrev): Eur J Health Econ

Title: The European Journal of Health Economics

Publisher: Springer Berlin Heidelberg (Berlin/Heidelberg )

ISSN (Print): 1618-7598

ISSN (Electronic): 1618-7601

Publication date (Electronic): 4 September 2017

Publication date PMC-release: 4 September 2017

Publication date (Print): 2018

Volume: 19

Issue: 6

Pages: 861-870

Affiliations

[1 ]ISNI 0000 0001 0481 6099, GRID grid.5012.6, Department of Health Services Research, CAPHRI School of Public Health and Primary Care, , Maastricht University, ; P.O. Box 616, 6200 MD Maastricht, The Netherlands

[2 ]ISNI 0000 0004 0396 792X, GRID grid.413972.a, Department of Research and Development, , Epilepsy Centre Kempenhaeghe, ; Heeze, The Netherlands

[3 ]ISNI 0000 0001 2322 6764, GRID grid.13097.3c, King’s Health Economics (KHE), , Institute of Psychiatry, Psychology and Neuroscience at King’s College London, ; London, UK

[4 ]ISNI 0000 0004 0480 1382, GRID grid.412966.e, Department of Neurology, Academic Centre for Epileptology, , Epilepsy Centre Kempenhaeghe and Maastricht University Medical Centre, ; Maastricht, The Netherlands

[5 ]ISNI 0000 0004 0480 1382, GRID grid.412966.e, School of Mental Health and Neuroscience, , Maastricht University Medical Center, ; Maastricht, The Netherlands

[6 ]ISNI 0000 0001 0481 6099, GRID grid.5012.6, School of Health Professions Education, Faculty of Health, Medicine and Life Sciences, , Maastricht University, ; Maastricht, The Netherlands

[7 ]ISNI 0000 0001 2322 6764, GRID grid.13097.3c, Department of Basic and Clinical Neuroscience, , Institute of Psychiatry, Psychology and Neuroscience at King’s College London, ; London, UK

[8 ]ISNI 0000 0001 0835 8259, GRID grid.416017.5, Trimbos Institute, , Netherlands Institute of Mental Health and Addiction, ; Utrecht, The Netherlands

[9 ]Duboisdomein 30, 6229 GT Maastricht, The Netherlands

Author information

Ben F. M. Wijnen http://orcid.org/0000-0001-7993-1905

Article

Publisher ID: 928

DOI: 10.1007/s10198-017-0928-0

PMC ID: 6008365

PubMed ID: 28871490

SO-VID: d82b64a8-1103-45d5-b2f3-21b0382a9e62

License:

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.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.

History

Date received : 26 April 2017

Date accepted : 25 August 2017

Funding

Funded by: Netherlands Organization for Health Research and Development

Award ID: grant application number 836011018

Award Recipient : Silvia M. A. A. Evers

Funded by: FundRef http://dx.doi.org/10.13039/501100000664, Health Technology Assessment Programme;

Award ID: project number 09/165/01

Award Recipient : Leone Ridsdale

Custom metadata

ScienceOpen disciplines: Economics of health & social care

Keywords: mapping,responsiveness,quality of life,epilepsy,d610 allocative efficiency,cost-benefit analysis

Data availability:

ScienceOpen disciplines: Economics of health & social care

Keywords: mapping, responsiveness, quality of life, epilepsy, d610 allocative efficiency, cost-benefit analysis

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

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Most cited references 24

A review of studies mapping (or cross walking) non-preference based measures of health to generic preference-based measures.

Comparative responsiveness of generic and specific quality-of-life instruments.

Mapping to obtain EQ-5D utility values for use in NICE health technology assessments.

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