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Men?s preferences for treatment of early stage prostate cancer: Results from a discrete choice experiment, CHERE Working Paper 2006/14

Listed author(s):
  • Madeleine King


    (CHERE, University of Technology, Sydney)

  • Rosalie Viney


    (CHERE, University of Technology, Sydney)

  • Ishrat Hossain


    (CHERE, University of Technology, Sydney)

  • David Smith

    (Cancer Council, NSW)

  • Sandra Fowler

    (CHERE, University of Technology, Sydney)

  • Elizabeth Savage


    (CHERE, University of Technology, Sydney)

  • Bruce Armstrong

    (University of Sydney)

Prostate cancer is the most common cancer in men in Australia; each year over 10,000 Australians are diagnosed with this disease. There are a number of treatment options for early stage prostate cancer (ESPC); radical prostatectomy, external beam radiotherapy, brachytherapy, hormonal therapy and combined therapy. Treatment can cause serious side-effects, including severe sexual and urinary dysfunction, bowel symptoms and fatigue. Furthermore, there is no evidence as yet to demonstrate that any of these treatments confers a survival gain over active surveillance (watchful waiting). While patient preferences should be important determinants in the type of treatment offered, little is known about patients? views of the relative tolerability of side effects and of the survival gains needed to justify these. To investigate this, a discrete choice experiment (DCE) was conducted in a sample of 357 men who had been treated for ESPC and 65 age-matched controls. The sample was stratified by treatment, with approximately equal numbers in each treatment group. The DCE included nine attributes: seven side-effects and two survival attributes (duration and uncertainty). An orthogonal fractional set of 108 scenarios from the full factorial was used to generate three versions of the questionnaire, with 18 scenarios per respondent. Multinomial logit (MNL) and mixed logit (MXL) models were estimated. A random intercept MXL model provided a significantly better fit to the data than the simple MNL model, and adding random coefficients for all attributes dramatically improved model fit. Each side-effect had a statistically significant mean effect on choice, as did survival duration. Most attributes had significant variance parameters, suggesting considerable heterogeneity among respondents in their preferences. To model this heterogeneity, we included men?s health-related quality of life scores following treatment as covariates to see whether their preferences were influenced by their previous treatment experience. This study demonstrate how DCEs can be used to quantify the trade-offs patients make between side-effects and survival gains. The results provide useful insights for clinicians who manage patients with ESPC, highlighting the importance of patient preferences in treatment decisions.

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File Function: First version, July 2006
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Paper provided by CHERE, University of Technology, Sydney in its series Working Papers with number 2006/14.

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Date of creation: Jul 2006
Handle: RePEc:her:chewps:2006/14
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Level 4, 645 Harris Street, Ultimo, NSW 2007

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  1. Andrews, Donald W. K., 1998. "Hypothesis testing with a restricted parameter space," Journal of Econometrics, Elsevier, vol. 84(1), pages 155-199, May.
  2. Brownstone, David & Train, Kenneth, 1998. "Forecasting new product penetration with flexible substitution patterns," Journal of Econometrics, Elsevier, vol. 89(1-2), pages 109-129, November.
  3. Bleichrodt, Han & Wakker, Peter & Johannesson, Magnus, 1997. "Characterizing QALYs by Risk Neutrality," Journal of Risk and Uncertainty, Springer, vol. 15(2), pages 107-114, November.
  4. Daniel McFadden & Kenneth Train, 2000. "Mixed MNL models for discrete response," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 15(5), pages 447-470.
  5. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387, December.
  6. Geweke, John & Keane, Michael, 2001. "Computationally intensive methods for integration in econometrics," Handbook of Econometrics,in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 5, chapter 56, pages 3463-3568 Elsevier.
  7. John M. Miyamoto & Peter P. Wakker & Han Bleichrodt & Hans J. M. Peters, 1998. "The Zero-Condition: A Simplifying Assumption in QALY Measurement and Multiattribute Utility," Management Science, INFORMS, vol. 44(6), pages 839-849, June.
  8. David Revelt & Kenneth Train, 1998. "Mixed Logit With Repeated Choices: Households' Choices Of Appliance Efficiency Level," The Review of Economics and Statistics, MIT Press, vol. 80(4), pages 647-657, November.
  9. Hall, Jane & Fiebig, Denzil G. & King, Madeleine T. & Hossain, Ishrat & Louviere, Jordan J., 2006. "What influences participation in genetic carrier testing?: Results from a discrete choice experiment," Journal of Health Economics, Elsevier, vol. 25(3), pages 520-537, May.
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