Please use this identifier to cite or link to this item: https://doi.org/10.1007/s10198-018-0987-x
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dc.titleImpact of mapped EQ-5D utilities on cost-effectiveness analysis: in the case of dialysis treatments
dc.contributor.authorYang, F
dc.contributor.authorDevlin, N
dc.contributor.authorLuo, N
dc.date.accessioned2020-10-27T10:02:52Z
dc.date.available2020-10-27T10:02:52Z
dc.date.issued2019
dc.identifier.citationYang, 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
dc.identifier.issn16187598
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/181162
dc.description.abstractObjectives: 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).
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.sourceUnpaywall 20201031
dc.subjectArticle
dc.subjectbootstrapping
dc.subjectcost effectiveness analysis
dc.subjectcost utility analysis
dc.subjectEuropean Quality of Life 5 Dimensions questionnaire
dc.subjecthuman
dc.subjectmajor clinical study
dc.subjectMarkov chain
dc.subjectperitoneal dialysis
dc.subjectpriority journal
dc.subjectquality adjusted life year
dc.subjectquality of life
dc.subjectreimbursement
dc.subjectrenal replacement therapy
dc.subjectShort Form 12
dc.subjectcost benefit analysis
dc.subjectdialysis
dc.subjecteconomics
dc.subjecthealth care cost
dc.subjecthemodialysis
dc.subjectprocedures
dc.subjectstatistics and numerical data
dc.subjectCost-Benefit Analysis
dc.subjectDialysis
dc.subjectHealth Care Costs
dc.subjectHumans
dc.subjectMarkov Chains
dc.subjectPeritoneal Dialysis
dc.subjectQuality-Adjusted Life Years
dc.subjectRenal Dialysis
dc.typeArticle
dc.contributor.departmentSAW SWEE HOCK SCHOOL OF PUBLIC HEALTH
dc.description.doi10.1007/s10198-018-0987-x
dc.description.sourcetitleEuropean Journal of Health Economics
dc.description.volume20
dc.description.issue1
dc.description.page99-105
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