Please use this identifier to cite or link to this item: https://doi.org/10.1007/s10198-018-0987-x
Title: Impact of mapped EQ-5D utilities on cost-effectiveness analysis: in the case of dialysis treatments
Authors: Yang, F
Devlin, N
Luo, N 
Keywords: Article
bootstrapping
cost effectiveness analysis
cost utility analysis
European Quality of Life 5 Dimensions questionnaire
human
major clinical study
Markov chain
peritoneal dialysis
priority journal
quality adjusted life year
quality of life
reimbursement
renal replacement therapy
Short Form 12
cost benefit analysis
dialysis
economics
health care cost
hemodialysis
procedures
statistics and numerical data
Cost-Benefit Analysis
Dialysis
Health Care Costs
Humans
Markov Chains
Peritoneal Dialysis
Quality-Adjusted Life Years
Renal Dialysis
Issue Date: 2019
Citation: Yang, F, Devlin, N, Luo, N (2019). Impact of mapped EQ-5D utilities on cost-effectiveness analysis: in the case of dialysis treatments. European Journal of Health Economics 20 (1) : 99-105. ScholarBank@NUS Repository. https://doi.org/10.1007/s10198-018-0987-x
Rights: Attribution 4.0 International
Abstract: Objectives: 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. Methods: 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. Results: 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. Conclusion: 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. © 2018, The Author(s).
Source Title: European Journal of Health Economics
URI: https://scholarbank.nus.edu.sg/handle/10635/181162
ISSN: 16187598
DOI: 10.1007/s10198-018-0987-x
Rights: Attribution 4.0 International
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