, Volume 31, Issue 7, pp 623–634 | Cite as

Random Regret-Based Discrete-Choice Modelling: An Application to Healthcare

  • Esther W. de Bekker-Grob
  • Caspar G. Chorus
Original Research Article



A new modelling approach for analysing data from discrete-choice experiments (DCEs) has been recently developed in transport economics based on the notion of regret minimization-driven choice behaviour. This so-called Random Regret Minimization (RRM) approach forms an alternative to the dominant Random Utility Maximization (RUM) approach. The RRM approach is able to model semi-compensatory choice behaviour and compromise effects, while being as parsimonious and formally tractable as the RUM approach.


Our objectives were to introduce the RRM modelling approach to healthcare-related decisions, and to investigate its usefulness in this domain.


Using data from DCEs aimed at determining valuations of attributes of osteoporosis drug treatments and human papillomavirus (HPV) vaccinations, we empirically compared RRM models, RUM models and Hybrid RUM–RRM models in terms of goodness of fit, parameter ratios and predicted choice probabilities.


In terms of model fit, the RRM model did not outperform the RUM model significantly in the case of the osteoporosis DCE data (p = 0.21), whereas in the case of the HPV DCE data, the Hybrid RUM–RRM model outperformed the RUM model (p < 0.05). Differences in predicted choice probabilities between RUM models and (Hybrid RUM-) RRM models were small. Derived parameter ratios did not differ significantly between model types, but trade-offs between attributes implied by the two models can vary substantially.


Differences in model fit between RUM, RRM and Hybrid RUM–RRM were found to be small. Although our study did not show significant differences in parameter ratios, the RRM and Hybrid RUM–RRM models did feature considerable differences in terms of the trade-offs implied by these ratios. In combination, our results suggest that RRM and Hybrid RUM–RRM modelling approach hold the potential of offering new and policy-relevant insights for health researchers and policy makers.


Choice Probability Parameter Ratio Compromise Effect Random Utility Maximization Compromise Alternative 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



The authors would like to thank the respondents for filling in the DCE questionnaires; Marie-Louise Essink-Bot and Ewout Steyerberg for their support in conducting the osteoporosis drug treatment DCE study; and Ida Korfage and Robine Hofman for the data collection for the HPV vaccination DCE study. Grant support was from the Department of Public Health, Erasmus MC – University Medical Centre Rotterdam, and The Netherlands Organisation for Scientific Research (The Netherlands Organisation for Scientific Research [NWO]; Talent Scheme Veni Grant No. 451-10-001). The views expressed by the authors in this paper are their own and not those of their funders.

Author Contributions

E.W. de Bekker-Grob designed and conducted the DCE studies, and drafted the manuscript. C.G. Chorus conceived the idea for the study, performed the analyses and contributed to the writing of the manuscript. Both authors have full access to all of the data in the study and can take responsibility for the integrity of the data and the accuracy of the data analysis.

Conflicts of interest

The authors declare that they have no competing interests.

Supplementary material

40273_2013_59_MOESM1_ESM.doc (853 kb)
Supplementary material 1 (DOC 853 kb)


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Copyright information

© Springer International Publishing Switzerland 2013

Authors and Affiliations

  1. 1.Department of Public HealthErasmus MC, University Medical Centre RotterdamRotterdamThe Netherlands
  2. 2.Transport and Logistics GroupDelft University of TechnologyDelftThe Netherlands

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