| Title |
A multi-criteria decision analysis perspective on the health economic evaluation of medical interventions
|
|---|---|
| Published in |
HEPAC Health Economics in Prevention and Care, July 2013
|
| DOI | 10.1007/s10198-013-0517-9 |
| Pubmed ID | |
| Authors |
Douwe Postmus, Tommi Tervonen, Gert van Valkenhoef, Hans L. Hillege, Erik Buskens |
| Abstract |
A standard practice in health economic evaluation is to monetize health effects by assuming a certain societal willingness-to-pay per unit of health gain. Although the resulting net monetary benefit (NMB) is easy to compute, the use of a single willingness-to-pay threshold assumes expressibility of the health effects on a single non-monetary scale. To relax this assumption, this article proves that the NMB framework is a special case of the more general stochastic multi-criteria acceptability analysis (SMAA) method. Specifically, as SMAA does not restrict the number of criteria to two and also does not require the marginal rates of substitution to be constant, there are problem instances for which the use of this more general method may result in a better understanding of the trade-offs underlying the reimbursement decision-making problem. This is illustrated by applying both methods in a case study related to infertility treatment. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| New Zealand | 1 | 33% |
| United States | 1 | 33% |
| Unknown | 1 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 67% |
| Science communicators (journalists, bloggers, editors) | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 67 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Greece | 1 | 1% |
| Switzerland | 1 | 1% |
| Unknown | 65 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 15 | 22% |
| Student > Master | 15 | 22% |
| Student > Ph. D. Student | 10 | 15% |
| Student > Doctoral Student | 7 | 10% |
| Other | 5 | 7% |
| Other | 9 | 13% |
| Unknown | 6 | 9% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 20 | 30% |
| Economics, Econometrics and Finance | 8 | 12% |
| Engineering | 7 | 10% |
| Business, Management and Accounting | 4 | 6% |
| Agricultural and Biological Sciences | 3 | 4% |
| Other | 13 | 19% |
| Unknown | 12 | 18% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 02 September 2014.
All research outputs
#16,045,990
of 25,368,786 outputs
Outputs from HEPAC Health Economics in Prevention and Care
#856
of 1,303 outputs
Outputs of similar age
#119,676
of 206,556 outputs
Outputs of similar age from HEPAC Health Economics in Prevention and Care
#17
of 29 outputs
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