Esther W de Bekker-Grob1, Caspar G Chorus. 1. Department of Public Health, Erasmus MC, University Medical Centre Rotterdam, PO Box 2040, 3000 CA, Rotterdam, The Netherlands. e.debekker@erasmusmc.nl
Abstract
BACKGROUND: 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. OBJECTIVES: Our objectives were to introduce the RRM modelling approach to healthcare-related decisions, and to investigate its usefulness in this domain. METHODS: 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. RESULTS: 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. CONCLUSION: 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.
BACKGROUND: 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. OBJECTIVES: Our objectives were to introduce the RRM modelling approach to healthcare-related decisions, and to investigate its usefulness in this domain. METHODS: 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. RESULTS: In terms of model fit, the RRM model did not outperform the RUM model significantly in the case of the osteoporosisDCE data (p = 0.21), whereas in the case of the HPVDCE 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. CONCLUSION: 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.
Authors: Esther W de Bekker-Grob; Robine Hofman; Bas Donkers; Marjolein van Ballegooijen; Theo J M Helmerhorst; Hein Raat; Ida J Korfage Journal: Vaccine Date: 2010-08-12 Impact factor: 3.641
Authors: Esther W de Bekker-Grob; Marie-Louise Essink-Bot; Willem Jan Meerding; Bart W Koes; Ewout W Steyerberg Journal: Pharmacoeconomics Date: 2009 Impact factor: 4.981
Authors: E W de Bekker-Grob; M L Essink-Bot; W J Meerding; H A P Pols; B W Koes; E W Steyerberg Journal: Osteoporos Int Date: 2008-01-08 Impact factor: 4.507
Authors: Jorien Veldwijk; Mattijs S Lambooij; Esther W de Bekker-Grob; Henriëtte A Smit; G Ardine de Wit Journal: PLoS One Date: 2014-11-03 Impact factor: 3.240
Authors: Jorien Veldwijk; Domino Determann; Mattijs S Lambooij; Janine A van Til; Ida J Korfage; Esther W de Bekker-Grob; G Ardine de Wit Journal: BMC Med Res Methodol Date: 2016-04-21 Impact factor: 4.615