| Title |
Utility Estimates of Disease-Specific Health States in Prostate Cancer from Three Different Perspectives
|
|---|---|
| Published in |
Applied Health Economics and Health Policy, October 2016
|
| DOI | 10.1007/s40258-016-0282-x |
| Pubmed ID | |
| Authors |
Katharine S. Gries, Dean A. Regier, Scott D. Ramsey, Donald L. Patrick |
| Abstract |
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. |
X Demographics
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Geographical breakdown
| Country | Count | As % |
|---|---|---|
| New Zealand | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Science communicators (journalists, bloggers, editors) | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 30 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Netherlands | 1 | 3% |
| Unknown | 29 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 8 | 27% |
| Student > Bachelor | 4 | 13% |
| Researcher | 4 | 13% |
| Student > Postgraduate | 2 | 7% |
| Student > Ph. D. Student | 1 | 3% |
| Other | 2 | 7% |
| Unknown | 9 | 30% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 8 | 27% |
| Nursing and Health Professions | 2 | 7% |
| Agricultural and Biological Sciences | 1 | 3% |
| Computer Science | 1 | 3% |
| Business, Management and Accounting | 1 | 3% |
| Other | 4 | 13% |
| Unknown | 13 | 43% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 17 October 2016.
All research outputs
#15,871,137
of 23,576,969 outputs
Outputs from Applied Health Economics and Health Policy
#566
of 799 outputs
Outputs of similar age
#203,869
of 321,430 outputs
Outputs of similar age from Applied Health Economics and Health Policy
#11
of 24 outputs
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