Advanced Search
MyIDEAS: Login to save this paper or follow this series

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

Contents:

Author Info

  • 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)

Abstract

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.

Download Info

If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
File URL: http://www.chere.uts.edu.au/pdf/wp2006_14.pdf
File Function: First version, July 2006
Download Restriction: no

Bibliographic Info

Paper provided by CHERE, University of Technology, Sydney in its series Working Papers with number 2006/14.

as in new window
Length:
Date of creation: Jul 2006
Date of revision:
Handle: RePEc:her:chewps:2006/14

Contact details of provider:
Postal: Level 4, 645 Harris Street, Ultimo, NSW 2007
Phone: +61 2 9514 9799
Fax: 61 2 9514 4730
Email:
Web page: http://www.chere.uts.edu.au
More information through EDIRC

Related research

Keywords: Prostate cancer; discrete choice experiment; preferences; quality of life;

Find related papers by JEL classification:

This paper has been announced in the following NEP Reports:

References

References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
as in new window
  1. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521747387, April.
  2. 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.
  3. Bleichrodt, Han & Wakker, Peter & Johannesson, Magnus, 1997. "Characterizing QALYs by Risk Neutrality," Journal of Risk and Uncertainty, Springer, vol. 15(2), pages 107-14, November.
  4. 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.
  5. 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.
  6. Donald W.K. Andrews, 1994. "Hypothesis Testing with a Restricted Parameter Space," Cowles Foundation Discussion Papers 1060R, Cowles Foundation for Research in Economics, Yale University.
  7. 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.
  8. Brownstone, David & Train, Kenneth, 1999. "Forecasting new product penetration with flexible substitution patterns," University of California Transportation Center, Working Papers qt1j6814b3, University of California Transportation Center.
  9. 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.
Full references (including those not matched with items on IDEAS)

Citations

Lists

This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.

Statistics

Access and download statistics

Corrections

When requesting a correction, please mention this item's handle: RePEc:her:chewps:2006/14. See general information about how to correct material in RePEc.

For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Liz Chinchen).

If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

If references are entirely missing, you can add them using this form.

If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.

If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.

Please note that corrections may take a couple of weeks to filter through the various RePEc services.