Including opt-out options in discrete choice experiments: issues to consider

Abstract

Background: Providing an opt-out alternative in discrete choice experiments can often be considered to be important for presenting real-life choice situations in different contexts, including health. However, insufficient attention has been given to how best to address choice behaviours relating to this opt-out alternative when modelling discrete choice experiments, particularly in health studies. Objective: The objective of this paper is to demonstrate how to account for different opt-out effects in choice models.We aim to contribute to a better understanding of how to model opt-out choices and show the consequences of addressing the effects in an incorrect fashion.We present our code written in the R statistics program so that others can explore these issues in their own data. Methods: In this practical guideline, we generate synthetic data on medication choice and use Monte Carlo simulation. We consider three different definitions for the opt-out alternative and four candidate models for each definition. We apply a frequentist-based multimodel inference approach and use performance indicators to assess the relative suitability of each candidate model in a range of settings. Results: We show that misspecifying the opt-out effect has repercussions for marginal willingness to pay estimation and the forecasting of market shares. Our findings also suggest a number of key recommendations for DCE practitioners interested in exploring these issues. Conclusions: There is no unique best way to analyse data collected from discrete choice experiments. Researchers should consider several models so that the relative support for different hypotheses of opt-out effects can be explored.