The Role of Attribute Screening and Choice Set Formation in Health Discrete Choice Experiments: Modeling the Impact of Benefit and Risk Attributes

Objectives: This study aimed to demonstrate the econometric modeling of benefit/risk-based choice set formation (CSF) within health-related discrete choice experiments. Methods: In 4 different case studies, first, a trade-off model was fitted; building on this, a screening model was fitted; and finally, a full CSF model was estimated. This final model allows for attributes to be used first to screen out alternatives from choice tasks before respondents’ trade-off attributes and make a choice among feasible alternatives. Educational level and health literacy of respondents were accounted for in all models. Results: Model fit in terms of log likelihood, pseudo-R², Akaike information criterion, and Bayesian information criterion improved from using only trade-off or screening models compared with CSF models in 3 of the 4 case studies. In those studies, significant screening behavior was identified that (1) affected trade-off inferences, (2) rejects the pure trade-off model, and (3) supports the existence of screening on the basis of benefit-risk profiles, and other attributes. Educational level and health literacy showed significant interactions with multiple attributes in all case studies. Conclusions: Choice modelers should pay close attention to noncompensatory respondent behavior when they include benefit or risk attributes in their discrete choice experiment. Further studies should investigate why and when respondents undertake screening behavior. Screening behavior in choice data analysis is always a possibility, so researchers should explore extensions of econometric models to reflect noncompensatory behavior. Assuming that benefit and risk attributes will only affect trade-off behavior is likely to lead to biased conclusions about benefit or risk-based behavior.