Including Opt-Out Options in Discrete Choice Experiments: Issues to Consider

Abstract

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. 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 statistical language so that others can explore these issues in their own data. 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. 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. 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.

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Data availability statement

For this paper, the data have been synthetically generated. Full details on the data-generating process and the code required to replicate our analysis are given in Appendix A of the ESM.

Notes

  1. 1.

    We note that random utility maximisation is not the only framework for modelling choices. Indeed, for certain decisions, other choice axioms may be better suited, such as regret minimisation. In this paper, we utilise the most widely used framework to analyse opt-out effects.

  2. 2.

    Note, however, that the derivation of the nested logit model does not necessarily imply that participants make choices in this hierarchical manner.

  3. 3.

    While this design ensures that all attribute levels can be estimated independently of each other, we recognise that a more efficient experimental design could have been used to minimise the variance of the parameters. However, in a Monte Carlo experiment with specified parameters it may be more appropriate to show that the results stand up in cases where the experimental design is not tailored too closely to the data-generating parameters. Indeed, this would be the case in a real-life empirical application.

  4. 4.

    This is sufficient for the purpose at hand since idiosyncratic simulation errors are not found to be large, as will be shown in Tables 3 and 4.

  5. 5.

    In this paper, we use the Bayesian information criterion . We derive this for each estimated model m in treatment t and replication r as follows: \(\text {IC}_{m_\mathrm{tr}}= \ln \left( N\right) K_{m_\mathrm{tr}} - 2\ln \left( \hat{\mathcal {L}}_{m_\mathrm{tr}}\right)\), where N is the number of choice observations, \(\hat{\mathcal {L}}_{m_\mathrm{tr}}\) is the maximised value of the likelihood function for model m in treatment t and replication r, and \(K_{m_\mathrm{tr}}\) is the number of estimated parameters associated with this model.

  6. 6.

    As noted when describing the independent availability logit model in Sect. 2.2.3, the alternatives taken into account by a (real or simulated) participant cannot be established with certainty. For the sake of comparison, we assume an alternative is deemed to be not in a participant’s consideration set if they never choose it in any of their eight choices.

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Acknowledgements

We thank the editor Christopher Carswell for his invitation to write this paper. We also thank four anonymous reviewers for their helpful comments and suggestions on previous versions of this paper. Any remaining errors or misinterpretations are solely the authors’ responsibility.

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Contributions

DC and SE contributed equally to all aspects of this paper, including the conceptualisation, data generation, analysis and drafting of the manuscript.

Corresponding author

Correspondence to Seda Erdem.

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The study was not supported by any external sources or funds.

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The study did not involve the collection of primary data or the use of secondary data sources, thus negating the need for ethical approval.

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Participants have been artifically generated as part of the Monte Carlo simulation, meaning that informed consent is not applicable.

Conflict of interest

Danny Campbell and Seda Erdem declare no conflicts of interest relevant to the content of this manuscript.

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Campbell, D., Erdem, S. Including Opt-Out Options in Discrete Choice Experiments: Issues to Consider. Patient 12, 1–14 (2019). https://doi.org/10.1007/s40271-018-0324-6

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