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Does One Size Fit All? Investigating Heterogeneity in Men’s Preferences for Benign Prostatic Hyperplasia Treatment Using Mixed Logit Analysis

Author

Listed:
  • Barbara Eberth

    (Health Economics Research Unit, University of Aberdeen, Aberdeen, UK, b.eberth@abdn.ac.uk)

  • Verity Watson

    (Health Economics Research Unit, University of Aberdeen, Aberdeen, UK)

  • Mandy Ryan

    (Health Economics Research Unit, University of Aberdeen, Aberdeen, UK)

  • Jenny Hughes

    (GlaxoSmithKline, Rathfarnham, Dublin, Ireland)

  • Gillian Barnett

    (Gillian Barnett and Associates, Donegal, Ireland)

Abstract

In this study, the authors demonstrate how mixed logit analysis of discrete choice experiment (DCE) data can provide information about unobserved preference heterogeneity. Their application investigates unobserved heterogeneity in men’s preferences for benign prostatic hyperplasia (BPH) treatment. They use a DCE to elicit preferences for seven characteristics of BPH treatment: time to symptom improvement, sexual and nonsexual treatment side effects, risks of acute urinary retention and surgery, cost of treatment, and reduction in prostate size. They investigate the importance of these characteristics and the trade-offs men are willing to make between them. Preferences are elicited from a sample of 100 men attending an outpatient clinic in Ireland. The authors find all treatment characteristics are significant determinants of treatment choice. There is significant preference heterogeneity in the population for four treatment characteristics: time to symptom improvement, treatment reducing prostate size, risk of surgery, and sexual side effects. The importance of preference heterogeneity at the policy level within the context of shared decision making is discussed.

Suggested Citation

  • Barbara Eberth & Verity Watson & Mandy Ryan & Jenny Hughes & Gillian Barnett, 2009. "Does One Size Fit All? Investigating Heterogeneity in Men’s Preferences for Benign Prostatic Hyperplasia Treatment Using Mixed Logit Analysis," Medical Decision Making, , vol. 29(6), pages 707-715, November.
    • Handle: RePEc:sae:medema:v:29:y:2009:i:6:p:707-715
    DOI: 10.1177/0272989X09341754
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    References listed on IDEAS

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    1. Greene, William H. & Hensher, David A., 2007. "Heteroscedastic control for random coefficients and error components in mixed logit," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 43(5), pages 610-623, September.
    2. McIntosh, E. & Ryan, M., 2002. "Using discrete choice experiments to derive welfare estimates for the provision of elective surgery: Implications of discontinuous preferences," Journal of Economic Psychology, Elsevier, vol. 23(3), pages 367-382, June.
    3. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, January.
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    Cited by:

    1. Mylene Lagarde & Nonglak Pagaiya & Viroj Tangcharoensathian & Duane Blaauw, 2013. "One Size Does Not Fit All: Investigating Doctors' Stated Preference Heterogeneity For Job Incentives To Inform Policy In Thailand," Health Economics, John Wiley & Sons, Ltd., vol. 22(12), pages 1452-1469, December.
    2. Milte, Rachel & Huynh, Elisabeth & Ratcliffe, Julie, 2019. "Assessing quality of care in nursing homes using discrete choice experiments: How does the level of cognitive functioning impact upon older people's preferences?," Social Science & Medicine, Elsevier, vol. 238(C), pages 1-1.
    3. Pradnya Naik-Panvelkar & Carol Armour & John Rose & Bandana Saini, 2012. "Patient Preferences for Community Pharmacy Asthma Services," PharmacoEconomics, Springer, vol. 30(10), pages 961-976, October.

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