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Scale Heterogeneity in Healthcare Discrete Choice Experiments: A Primer

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Abstract

Discrete choice experiments (DCEs) are used to quantify the preferences of specified sample populations for different aspects of a good or service and are increasingly used to value interventions and services related to healthcare. Systematic reviews of healthcare DCEs have focussed on the trends over time of specific design issues and changes in the approach to analysis, with a more recent move towards consideration of a specific type of variation in preferences within the sample population, called taste heterogeneity, noting rises in the popularity of mixed logit and latent class models. Another type of variation, called scale heterogeneity, which relates to differences in the randomness of choice behaviour, may also account for some of the observed ‘differences’ in preference weights. The issue of scale heterogeneity becomes particularly important when comparing preferences across subgroups of the sample population as apparent differences in preferences could be due to taste and/or choice consistency. This primer aims to define and describe the relevance of scale heterogeneity in a healthcare context, and illustrate key points, with a simulated data set provided to readers in the Online appendix.

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Notes

  1. In some studies, preferences are allowed to vary by individual, introducing the subscript, n: \(\beta_{n,k}\). This primer focuses on scale heterogeneity and not preference heterogeneity. For this reason, issues relating to the joint identification of preference and scale heterogeneity are not addressed in this primer. We refer readers to Hess and Train [36] for a description unsuitable for this primer.

  2. The MNL is often used interchangeably with ‘conditional logit’. In this case, we use the term ‘multinomial logit’ or ‘MNL’ as this is commonly used in the literature cited, but acknowledge estimations in Stata for this definition will use the clogit command.

  3. Comparisons of coefficients estimated with different models can also be problematic. For example, estimating a probit model with a standard normal distribution (with a variance of 1), and a logit model with a standard logistic distribution with a variance of \(\frac{{\pi^{2} }}{3}\), will result in a difference of estimated coefficients of \(\sqrt {\frac{{\pi^{2} }}{3}}\) or 1.8. For a worked example, see Part D of the Technical Appendix, and, for more details, see Chapters 2 and 3 of Train [37].

  4. For simplicity, alternative specific constants are not included in this model. It is noted in some literature that alternative specific constants should also not be included on these coefficient plots [19].

  5. For a more detailed discussion of issues that arise when comparing preference data, and a formal test to identify scale heterogeneity, we refer readers to chapters 8 and 13 of Louviere et al. [13], and Swait and Louviere [19].

  6. Part E of the Technical Appendix generates data with different scale and preference parameters in the two samples, and example estimation where the equivalence of the preference parameters is rejected, even once one has controlled for scale.

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Acknowledgements

The authors wish to thank Dr Arne Hole from the University of Sheffield for reading and commenting on a draft of the manuscript.

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Authors and Affiliations

  1. Manchester Centre for Health Economics, The University of Manchester, Manchester, UK

    Caroline M. Vass, Stuart Wright & Katherine Payne

  2. UWA School of Agriculture and Environment, University of Western Australia, Crawley, WA, Australia

    Michael Burton

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  1. Caroline M. Vass
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  2. Stuart Wright
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Contributions

All authors were involved in the drafting and editing of the manuscript, and Michael Burton was also involved in the simulation of choice data.

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Correspondence to Caroline M. Vass.

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Conflict of interest

Caroline M. Vass and Katherine Payne were supported in the preparation and submission of this paper by Mind the Risk, from The Swedish Foundation for Humanities and Social Sciences. The views and opinions expressed are those of the authors and are not necessarily those of other Mind the Risk members or The Swedish Foundation for Humanities and Social Sciences. Stuart Wright and Michael Burton declare that they have no conflicts of interest.

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Vass, C.M., Wright, S., Burton, M. et al. Scale Heterogeneity in Healthcare Discrete Choice Experiments: A Primer. Patient 11, 167–173 (2018). https://doi.org/10.1007/s40271-017-0282-4

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