Do country-specific preference weights matter in the choice of mapping algorithms? The case of mapping the Diabetes-39 onto eight country-specific EQ-5D-5L value sets
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To develop mapping algorithms that transform Diabetes-39 (D-39) scores onto EQ-5D-5L utility values for each of eight recently published country-specific EQ-5D-5L value sets, and to compare mapping functions across the EQ-5D-5L value sets.
Data include 924 individuals with self-reported diabetes from six countries. The D-39 dimensions, age and gender were used as potential predictors for EQ-5D-5L utilities, which were scored using value sets from eight countries (England, Netherland, Spain, Canada, Uruguay, China, Japan and Korea). Ordinary least squares, generalised linear model, beta binomial regression, fractional regression, MM estimation and censored least absolute deviation were used to estimate the mapping algorithms. The optimal algorithm for each country-specific value set was primarily selected based on normalised root mean square error (NRMSE), normalised mean absolute error (NMAE) and adjusted-r2. Cross-validation with fivefold approach was conducted to test the generalizability of each model.
The fractional regression model with loglog as a link function consistently performed best in all country-specific value sets. For instance, the NRMSE (0.1282) and NMAE (0.0914) were the lowest, while adjusted-r2 was the highest (52.5%) when the English value set was considered. Among D-39 dimensions, the energy and mobility was the only one that was consistently significant for all models.
The D-39 can be mapped onto the EQ-5D-5L utilities with good predictive accuracy. The fractional regression model, which is appropriate for handling bounded outcomes, outperformed other candidate methods in all country-specific value sets. However, the regression coefficients differed reflecting preference heterogeneity across countries.
KeywordsMapping Diabetes-39 EQ-5D-5L HRQoL Utility QALY
Compliance with ethical standards
Conflict of interest
The authors declare that they have no conflict of interest.
Ethical approval was granted by the Monash University Human Research Ethics Committee [Reference No. CF11/ 3192–2011001748]. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.
Informed consent was obtained from all individual participants included in the study.
- 4.Brazier, J., Ratcliffe, J., Saloman, J., & Tsuchiya, A. (2017). Measuring and valuing health benefits for economic evaluation. Oxford: Oxford University Press.Google Scholar
- 5.WHO. (2016). Global report on diabetes. France: World Health Organization.Google Scholar
- 6.IDF. (2015). Diabetes Atlas (7th edn.). Brussels: International Diabetes Federation (IDF).Google Scholar
- 7.Drummond, M. F., Sculpher, M. J., Torrance, G. W., O’Brien, B. J., & Stoddart, G. L. (2015). Methods for the economic evaluation of health care programme (4th edn.). Oxford: Oxford University Press: Oxford.Google Scholar
- 12.NICE (National Institute for Health and Care Excellence). (2013). Guide to the methods of technology appraisal. London: National Health Service. Retrieved September 18, 2017, from http://www.nice.org.uk.
- 15.van Hout, B., Janssen, M. F., Feng, Y.-S., Kohlmann, T., Busschbach, J., Golicki, D., Lloyd, A., Scalone, L., Kind, P., & Pickard, A. S. (2012). Interim scoring for the EQ-5D-5L: Mapping the EQ-5D-5L to EQ-5D-3L Value Sets. Value in Health, 15(5), 708–715. https://doi.org/10.1016/j.jval.2012.02.008.CrossRefPubMedGoogle Scholar
- 16.Augustovski, F., Rey-Ares, L., Irazola, V., Garay, O. U., Gianneo, O., Fernandez, G., Morales, M., Gibbons, L., & Ramos-Goni, J. M. (2015). An EQ-5D-5L value set based on Uruguayan population preferences. Quality of Life Research. https://doi.org/10.1007/s11136-015-1086-4.PubMedCrossRefGoogle Scholar
- 23.Shiroiwa, T., Ikeda, S., Noto, S., Igarashi, A., Fukuda, T., Saito, S., & Shimozuma, K. (2016). Comparison of value set based on DCE and/or TTO data: Scoring for EQ-5D-5L health states in Japan. Value in Health, 19(5), 648–654. https://doi.org/10.1016/j.jval.2016.03.1834.CrossRefPubMedGoogle Scholar
- 24.Petrou, S., Rivero-Arias, O., Dakin, H., Longworth, L., Oppe, M., Froud, R., & Gray, A. (2015). Preferred reporting items for studies mapping onto preference-based outcome measures: The MAPS statement. Health and Quality of Life Outcomes, 13(1), 106. https://doi.org/10.1186/s12955-015-0305-6.CrossRefPubMedPubMedCentralGoogle Scholar
- 25.Richardson, J., Iezzi, A., & Maxwell, A. (2012). Cross-national comparison of twelve quality of life instruments: Mic Paper 1 background, questions, instruments. Research Paper 76. Retrieved November 23, 2017, from https://www.aqol.com.au/papers/researchpaper76.pdf.
- 26.Kaambwa, B., Chen, G., Ratcliffe, J., Iezzi, A., Maxwell, A., & Richardson, J. (2017). Mapping between the Sydney Asthma Quality of Life Questionnaire (AQLQ-S) and five multi-attribute utility instruments (MAUIs). Pharmacoeconomics, 35(1), 111–124. https://doi.org/10.1007/s40273-016-0446-4.CrossRefPubMedGoogle Scholar
- 27.Mihalopoulos, C., Chen, G., Iezzi, A., Khan, M. A., & Richardson, J. (2014). Assessing outcomes for cost-utility analysis in depression: Comparison of five multi-attribute utility instruments with two depression-specific outcome measures. British Journal of Psychiatry, 205(5), 390–397. https://doi.org/10.1192/bjp.bp.113.136036.CrossRefPubMedGoogle Scholar
- 29.Herdman, M., Gudex, C., Lloyd, A., Janssen, M., Kind, P., Parkin, D., Bonsel, G., & Badia, X. (2011). Development and preliminary testing of the new five-level version of EQ-5D (EQ-5D-5L). Quality of Life Research, 20(10), 1727–1736. https://doi.org/10.1007/s11136-011-9903-x.CrossRefPubMedPubMedCentralGoogle Scholar
- 33.Jobson, J. (2012). Applied multivariate data analysis: Volume II: Categorical and multivariate methods. New York: Springer.Google Scholar
- 36.Brazier, J. E., Yang, Y., Tsuchiya, A., & Rowen, D. L. (2010). A review of studies mapping (or cross walking) non-preference based measures of health to generic preference-based measures. The European Journal of Health Economics. https://doi.org/10.1007/s10198-009-0168-z.PubMedCrossRefGoogle Scholar
- 48.Paolino, P. (2001). Maximum likelihood estimation of models with beta distributed dependent variables. Political Analysis. https://doi.org/10.1093/oxfordjournals.pan.a004873.CrossRefGoogle Scholar
- 49.Hunger, M., Baumert, J., & Holle, R. Analysis of SF-6D Index Data: Is beta regression appropriate? Value in Health, 14(5), 759–767. https://doi.org/10.1016/j.jval.2010.12.009.
- 55.Boland, M. R. S., van Boven, J. F. M., Kocks, J. W. H., van der Molen, T., Goossens, L. M., Chavannes, N. H., & Rutten-van Mölken, M. P. M. H. (2015). Mapping the clinical chronic obstructive pulmonary disease questionnaire onto generic preference-based EQ-5D values. Value in Health, 18(2), 299–307. https://doi.org/10.1016/j.jval.2014.11.006.CrossRefPubMedGoogle Scholar