Applied Health Economics and Health Policy

, Volume 15, Issue 3, pp 375–384 | Cite as

Utility Estimates of Disease-Specific Health States in Prostate Cancer from Three Different Perspectives

  • Katharine S. GriesEmail author
  • Dean A. Regier
  • Scott D. Ramsey
  • Donald L. Patrick
Original Research Article



To develop a statistical model generating utility estimates for prostate cancer specific health states, using preference weights derived from the perspectives of prostate cancer patients, men at risk for prostate cancer, and society.


Utility estimate values were calculated using standard gamble (SG) methodology. Study participants valued 18 prostate-specific health states with the five attributes: sexual function, urinary function, bowel function, pain, and emotional well-being. Appropriateness of model (linear regression, mixed effects, or generalized estimating equation) to generate prostate cancer utility estimates was determined by paired t-tests to compare observed and predicted values. Mixed-corrected standard SG utility estimates to account for loss aversion were calculated based on prospect theory.


132 study participants assigned values to the health states (n = 40 men at risk for prostate cancer; n = 43 men with prostate cancer; n = 49 general population). In total, 792 valuations were elicited (six health states for each 132 participants). The most appropriate model for the classification system was a mixed effects model; correlations between the mean observed and predicted utility estimates were greater than 0.80 for each perspective.


Developing a health-state classification system with preference weights for three different perspectives demonstrates the relative importance of main effects between populations. The predicted values for men with prostate cancer support the hypothesis that patients experiencing the disease state assign higher utility estimates to health states and there is a difference in valuations made by patients and the general population.


Prostate Cancer Mixed Effect Model Generalize Estimate Equation Standard Gamble Preference Weight 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Compliance with Ethical Standards

Funding was provided by the Biobehavioral Cancer Prevention and Control Training Program from the National Cancer Institute (R25CA092408; PI: Donald Patrick). The authors Katharine Gries, Dean Regier, Scott Ramsey, and Donald Patrick have no conflicts of interest in regard to this study. Institutional Review Board approval from the University of Washington and the Fred Hutchinson Cancer Research Center was obtained and the study was performed in accordance with the ethical standards of the Declaration of Helsinki. Informed consent was obtained from all participants included in the study, prior to any study related activities.

Author Contributions

Katharine Gries contributed to the study design, collection of data, statistical analysis, and writing of the manuscript. Dean Regier contributed to the study design, statistical analysis, and writing of the manuscript. Scott Ramsey and Donald Patrick contributed to the study design and writing of the manuscript.

Supplementary material

40258_2016_282_MOESM1_ESM.docx (19 kb)
Supplementary material 1 (DOCX 19 kb)
40258_2016_282_MOESM2_ESM.docx (26 kb)
Supplementary material 2 (DOCX 26 kb)


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Katharine S. Gries
    • 1
    Email author
  • Dean A. Regier
    • 2
    • 3
  • Scott D. Ramsey
    • 4
  • Donald L. Patrick
    • 5
  1. 1.EvideraSeattleUSA
  2. 2.Canadian Centre for Applied Research in Cancer ControlBC Cancer Agency Research CentreVancouverCanada
  3. 3.School of Population and Public HealthUniversity of British ColumbiaVancouverCanada
  4. 4.Fred Hutchinson Cancer Research CenterSeattleUSA
  5. 5.Department of Health ServicesUniversity of WashingtonSeattleUSA

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