| Title |
Impact of mapped EQ-5D utilities on cost-effectiveness analysis: in the case of dialysis treatments
|
|---|---|
| Published in |
HEPAC Health Economics in Prevention and Care, June 2018
|
| DOI | 10.1007/s10198-018-0987-x |
| Pubmed ID | |
| Authors |
Fan Yang, Nancy Devlin, Nan Luo |
| Abstract |
This study aimed to evaluate the performance of EQ-5D data mapped from SF-12 in terms of estimating cost effectiveness in cost-utility analysis (CUA). The comparability of SF-6D (derived from SF-12) was also assessed. Incremental quality-adjusted life years (QALYs) and incremental cost-effectiveness ratios (ICERs) were calculated based on two Markov models assessing the cost effectiveness of haemodialysis (HD) and peritoneal dialysis (PD) using utility values based on EQ-5D-5L, EQ-5D using three direct-mapping algorithms and two response-mapping algorithms (mEQ-5D), and SF-6D. Bootstrap method was used to estimate the 95% confidence interval (percentile method) of incremental QALYs and ICERs with 1000 replications for the utilities. In both models, compared to the observed EQ-5D values, mEQ-5D values expressed much lower incremental QALYs (range - 14.9 to - 33.2%) and much higher ICERs (range 17.5 to 49.7%). SF-6D also estimated lower incremental QALYs (- 29.0 and - 14.9%) and higher ICERs (40.9 and 17.5%) than did the observed EQ-5D. The 95% confidence interval of incremental QALYs and ICERs confirmed the lower incremental QALYs and higher ICERs estimated using mEQ-5D and SF-6D. Compared to observed EQ-5D, EQ-5D mapped from SF-12 and SF-6D would under-estimate the QALYs gained in cost-utility analysis and thus lead to higher ICERs. It would be more sensible to conduct CUA studies using directly collected EQ-5D data and to designate one single preference-based measure as reference case in a jurisdiction to achieve consistency in healthcare decision-making. |
X Demographics
The data shown below were collected from the profiles of 20 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United Kingdom | 9 | 45% |
| Australia | 2 | 10% |
| Netherlands | 1 | 5% |
| Saudi Arabia | 1 | 5% |
| New Zealand | 1 | 5% |
| Spain | 1 | 5% |
| Unknown | 5 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 13 | 65% |
| Scientists | 6 | 30% |
| Science communicators (journalists, bloggers, editors) | 1 | 5% |
Mendeley readers
The data shown below were compiled from readership statistics for 49 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 49 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 10 | 20% |
| Student > Master | 6 | 12% |
| Researcher | 5 | 10% |
| Student > Ph. D. Student | 4 | 8% |
| Student > Doctoral Student | 3 | 6% |
| Other | 7 | 14% |
| Unknown | 14 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 9 | 18% |
| Nursing and Health Professions | 8 | 16% |
| Pharmacology, Toxicology and Pharmaceutical Science | 4 | 8% |
| Economics, Econometrics and Finance | 3 | 6% |
| Business, Management and Accounting | 2 | 4% |
| Other | 10 | 20% |
| Unknown | 13 | 27% |
Attention Score in Context
This research output has an Altmetric Attention Score of 12. 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 05 September 2018.
All research outputs
#2,955,429
of 25,382,440 outputs
Outputs from HEPAC Health Economics in Prevention and Care
#170
of 1,303 outputs
Outputs of similar age
#57,278
of 341,958 outputs
Outputs of similar age from HEPAC Health Economics in Prevention and Care
#7
of 17 outputs
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