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Explore preference heterogeneity for treatment among people with Type 2 diabetes: A comparison of random-parameters and latent-class estimation techniques

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  • Zhou, Mo
  • Bridges, John F.P.

Abstract

There has been an increasing interest in studying patient preference heterogeneity to support regulatory decision-making. While the traditional mixed logit (MXL) and the latent class logit (LCL) models have been commonly used to analyze preference heterogeneity in discrete choice data, they have limitations. This study empirically compares a random effects latent class logit (RELCL) model to the traditional approaches using preference data from a discrete-choice experiment among patients with Type 2 diabetes. Each survey contained 18 pairs of hypothetical diabetes medications that differed in six attributes. Sensitivity analysis is also performed to explore under what circumstances RELCL outperforms LCL.

Suggested Citation

  • Zhou, Mo & Bridges, John F.P., 2019. "Explore preference heterogeneity for treatment among people with Type 2 diabetes: A comparison of random-parameters and latent-class estimation techniques," Journal of choice modelling, Elsevier, vol. 30(C), pages 38-49.
    • Handle: RePEc:eee:eejocm:v:30:y:2019:i:c:p:38-49
    DOI: 10.1016/j.jocm.2018.11.002
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    References listed on IDEAS

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    Cited by:

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