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Modeling Heterogeneity in Patients’ Preferences for Psoriasis Treatments in a Multicountry Study: A Comparison Between Random-Parameters Logit and Latent Class Approaches

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Abstract

Background

Either a random-parameters logit (RPL) or latent class (LC) model can be used to model or explain preference heterogeneity in discrete-choice experiment (DCE) data. The former assumes continuous distribution of preferences across the sample, while the latter assumes a discrete distribution. This study compared RPL and LC models to explore preference heterogeneity when analyzing patient preferences for psoriasis treatments.

Methods

Using DCE data collected from respondents with moderate-to-severe plaque psoriasis, we calculated and compared preference weights derived from RPL and LC models. We then compared how RPL and LC explain preference heterogeneity by exploring differences across subgroups defined by observed characteristics (i.e., country, age, gender, marital status, and psoriasis severity).

Results

While RPL and LC models resulted in the same mean preference weights, different preference-heterogeneity patterns emerged from the two approaches. In both models, country of residence and self-reported disease severity could be linked to systematic differences in preferences. The RPL also identified gender and marital status, but not age, as sources of heterogeneity; the LC membership probability model indicated that age was a significant factor, but not gender or marital status.

Conclusions

Using data from a psoriasis patient survey to compare two widely used methods for exploring heterogeneity identified differences in results between stated-preferences: subgroup analysis in the RPL model and inclusion of subgroup characteristics in the class membership probability function of the LC model. Researchers should model data using the most adaptable approach to address the initial study question.

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Data Availability

The datasets generated and analyzed during the current study are not publicly available due to third-party restrictions. The data may be available from Daniel Saure on reasonable request and with permission of Eli Lilly.

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Acknowledgements

The authors gratefully acknowledge Kimberly Moon and Ginger Powell of RTI Health Solutions for overall project management for this study and Kate Lothman of RTI Health Solutions for her help during the development of this manuscript.

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Authors and Affiliations

  1. RTI Health Solutions, Health Preference Assessment, Forsyth House, Cromac Square, Belfast, BT2 8LA, UK

    Marco Boeri

  2. Eli Lilly and Company, Bad Homburg, Germany

    Daniel Saure & Alexander Schacht

  3. Eli Lilly and Company, Vienna, Austria

    Elisabeth Riedl

  4. RTI Health Solutions, Health Preference Assessment, Research Triangle Park, NC, USA

    Brett Hauber

Authors
  1. Marco Boeri
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  2. Daniel Saure
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  3. Alexander Schacht
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  4. Elisabeth Riedl
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  5. Brett Hauber
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Contributions

DS, AS, and ER were involved in the study and in defining research questions. MB and BH conducted the statistical analysis and interpreted the results. MB led development of the manuscript, with input from BH. All authors contributed to drafting and revising the manuscript, gave final approval of the version to be published, and agree to be accountable for all aspects of the work.

Corresponding author

Correspondence to Marco Boeri.

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Conflict of interest

Marco Boeri and Brett Hauber are employees of RTI Health Solutions, which received funding from Eli Lilly and Company to conduct the analyses that are the subject of this manuscript. Daniel Saure and Elisabeth Riedl are employees of Eli Lilly and Company. Alexander Schacht was employee of Eli Lilly at the time of writing the manuscript and is now an employee of UCB Biosciences.

Funding

Financial support for this study was provided by Eli Lilly and Company. The funding agreement ensured the authors’ independence in designing the study, interpreting the data, writing, and publishing the report. The following authors are employed by the sponsor: Daniel Saure and Elisabeth Riedl. Alexander Schacht was an employee of Eli Lilly at the time of writing the manuscript and is now employee of UCB Biosciences. Marco Boeri and Brett Hauber are employees of RTI Health Solutions. The initial work from which the manuscript was generated was presented in 2018 at the Promoting Statistical Insight (PSI) conference in Amsterdam, the Netherlands (Saure D, Boeri M, Thorn K, Schacht A. Make conjoint analyses your standard approach understand how patient preferences vary across different subgroups. 2018 PSI Conference; 5 June 2018; Amsterdam). The presentation included initial results which were then used as starting point for this manuscript.

Ethics and Informed Consent

The research was carried out in compliance with national laws protecting respondents’ personal data and with the Codes of Conduct of the European Society for Opinion and Market Research, the European Pharmaceutical Marketing Research Association, and the British Healthcare Business Intelligence Association. This study was deemed exempt from ethics committee approval in every country where the study was conducted, in line with the codes of conduct of the relevant market research societies. Respondents were recruited among a pool of subjects who had previously agreed to be contacted for market research. At the beginning of the survey, respondents were informed about the general purpose and the objectives of the research, given contact details of a staff member available in case of questions or concerns, and asked to explicitly give consent to participate in the study. The study team was not allowed to contact respondents for purposes other than for answering the survey instrument. In order to protect privacy, providing follow-ups to respondents was also not allowed; however, respondents could contact the study team through the contact received in case of questions of concerns. No clinical data were collected in this study.

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Boeri, M., Saure, D., Schacht, A. et al. Modeling Heterogeneity in Patients’ Preferences for Psoriasis Treatments in a Multicountry Study: A Comparison Between Random-Parameters Logit and Latent Class Approaches. PharmacoEconomics 38, 593–606 (2020). https://doi.org/10.1007/s40273-020-00894-7

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