Purpose: To introduce a new modeling approach based on the notion of regret minimization driven choice behavior for analyzing data from discrete choice experiments (DCEs) in health care. This so-called Random Regret Minimization (RRM) approach has been recently developed in transport economics, and forms an alternative to the dominant Random Utility Maximization (RUM) approach. The RRM approach is able to model semi-compensatory choice behavior and compromise effects, while being as parsimonious and formally tractable as the RUM approach.
Method: 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, relative attribute importance, and predicted choice probabilities.
Result: The RRM-model outperformed the RUM-model in case of the osteoporosis DCE (p<0.05), whereas in case of the HPV DCE data the Hybrid RUM-RRM-model outperformed the RUM-model (p<0.05). Although the differences in model fit between RUM-models and (Hybrid RUM-) RRM-models were small, differences in terms of the derived estimated relative importance of attributes (up to 50%) as well as in terms of predicted choice probabilities for various choice-alternatives were considerable.
Conclusion: RRM-models or Hybrid RUM-RRM-models can produce a significantly better fit with DCE data than RUM-models. They potentially result in fairly different estimated relative attribute importance and predicted choice probabilities. This implies that the RRM and Hybrid RRM-RUM hold the potential of offering new and policy-relevant insights for the health researchers and policy-makers.
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