Full-text resources of CEJSH and other databases are now available in the new Library of Science.
Visit https://bibliotekanauki.pl


2017 | 12 | 75-89

Article title

Comparing the Crisp and Fuzzy Approaches to Modelling Preferences Towards Health States


Title variants

Languages of publication



Understanding societal preferences towards health is vital in public decisions on nancing health technologies. Thought experiments in which respondents choose between health states are used to understand the importance of individual criteria. Competing models of preference structure can be compared by their ability to explain empirical observations. One of the key challenges when constructing such models is that they have to aggregate preferences dened in multiple-criteria space. In the present paper, we test whether treating the impact of health worsening (dened using EQ-5D-5L descriptive system, i.e. decomposing health status in ve criteria) as a fuzzy concept can improve the model fit. To test if fuzzy approach to multiple-criteria preferences aggregation is valid, we compare a standard, crisp model (SM) with two models using fuzzy sets (JKL, previously proposed in the literature; and FMN introduced here). We nd FMN better than SM, and SM better than JKL. Anxiety/depression and pain/discomfort seem to weigh most in preferences. According to FMN, self-care and usual activities are associated with largest imprecision in preferences. The respondents are susceptible to framing eects when time unit is changed: e.g. measuring the duration in days results in short intervals mattering more than when expressed as weeks. We conclude that the fuzzy-based framework is promising, but requires careful work on the exact specication.






Physical description


  • SGH Warsaw School of Economics. Decision Analysis and Support Unit. Warsaw, Poland
  • SGH Warsaw School of Economics. Decision Analysis and Support Unit. Warsaw, Poland


  • Attema A.E., Brouwer W.B. (2010), On the (not so) Constant Proportional Trade-off in TTO, Quality of Life Research, 19(4), 489-497.
  • Bansback N., Brazier J., Tsuchiya A., Anis A. (2012), Using a Discrete Choice Experiment to Estimate Health State Utility Values, Journal of Health Economics, 31, 306-318.
  • Beresniak A., Medina-Lara A., Auray J.P., De Wever A., Praet J.C., Tarricone R., Torbica A., Dupont D., Lamure M., Duru G. (2015), Validation of the Underlying Assumptions of the Quality-adjusted Life-years Outcome: Results from the ECHOUTCOME European Project, Pharmacoeconomics, 33(1), 61-69.
  • Bezanson J., Edelman A., Karpinski S., Shah V.B. (2017), Julia: A Fresh Approach to Numerical Computing, SIAM Review, 59, 65-98.
  • Bleichrodt H., Wakker P., Johannesson M. (1997), Characterizing QALYs by Risk Neutrality, Journal of Risk and Uncertainty, 15, 107-114.
  • Brooks R., De Charro F. (1996), EuroQol: The Current State of Play, Health Policy, 37, 53-72.
  • Du K.-L., Swamy M.N.S. (2016), Search and Optimization by Metaheuristics, Springer.
  • Gao F., Han L. (2012), Implementing the Nelder-Mead Simplex Algorithm with Adaptive Parameters, Computational Optimization and Applications, 51, 259-277.
  • 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: An International Journal of Quality of Life Aspects of Treatment, Care and Rehabilitation, 20(10), 1727-1736.
  • Jakubczyk M. (2009), Impact of Complementarity and Heterogeneity on Health Related Utility of Life, Central European Journal of Economic Modelling and Econometrics, 1, 139-156.
  • Jakubczyk M. (2015), Using a Fuzzy Approach in Multi-criteria Decision Making with Multiple Alternatives in Health Care, Multiple Criteria Decision Making, 10, 65-81.
  • Jakubczyk M., Craig B.M., Barra M., Groothuis-Oudshoorn C.G., Hartman J.D., Huynh E., Ramos-Go~ni J.M., Stolk E.A., Rand-Hendriksen K. (2017), Choice De_nes Value: A Predictive Modeling Competition in Health Preference Research, Value in Health, doi: 10.1016/j.jval.2017.09.016.
  • Jakubczyk M., Kami_nski B. (2017), Fuzzy Approach to Decision Analysis with Multiple Criteria and Uncertainty in Health Technology Assessment, Annals of Operations Research, 251, 301-324.
  • Jakubczyk M., Kami_nski B., Lewandowski M. (2017), Eliciting Fuzzy Preferences Towards Health States with Discrete Choice Experiments [in:] Studies in Systems, Decision and Control. Vol 125. Complex Systems: Solutions and Challenges in Economics, Management and Engineering, eds. C. Berger-Vachon, A.M.G. Lafuente, J. Kacprzyk, Y. Kondratenko, C.F. Merig_o, Springer, 131-146.
  • Marti R. (2003), Multi-Start Methods [in:] Handbook of Metaheuristics, eds. F.W. Glover, G.A. Kochenberger, Springer, 355-368.
  • Menard S.W. (2002), Applied Logistic Regression (2nd ed.), SAGE.
  • Miyamoto J.M., Wakker P.P., Bleichrodt H., Peters H.J.M. (1998), The Zero-Condition: A Simplifying Assumption in QALY Measurement and Multiattribute Utility, Management Science, 44(6), 839-849.
  • Pettitt D.A., Raza S., Naughton B., Roscoe A., Ramakrishnan A., Ali A., Davies B., Dopson S., Hollander G., Smith J.A., Brindley D.A. (2016), The Limitations of QALY: A Literature Review, Journal of Stem Cell Research & Therapy, 6(4), 334.
  • Weinstein M.C., Torrance G., McGuire A. (2009), QALYs: The Basics, Value in Health, 12 (S1), S5-S9.
  • White J.M., Mogensen P.K., Holy T., Riseth A.N., Lubin M., Stocker Ch., Ortner Ch., Johnson B., Noack A., Yu Y., Carlsson K., Lin D., Covert T.R., Rock R., Regier J., Kuhn B., Williams A., Ryan, Smith D., Anantharaman R., Gomez M., Revels J., Dunning I., MacMillen D., Rackauckas Ch., Legat B., Levitt A., Stukalov A., Petrov A., Mahajan A. (2017), JuliaNLSolvers/Optim.jl, doi: 10.5281/zenodo.1035790.
  • Zadeh L. (1965), Fuzzy Sets, Information and Control, 8(3), 338-353.

Document Type

Publication order reference



YADDA identifier

JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.