File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Mapping the kidney disease quality of life 36-item short form survey (KDQOL-36) to the EQ-5D-3L and the EQ-5D-5L in patients undergoing dialysis

TitleMapping the kidney disease quality of life 36-item short form survey (KDQOL-36) to the EQ-5D-3L and the EQ-5D-5L in patients undergoing dialysis
Authors
KeywordsDialysis
EQ-5D-3L
EQ-5D-5L
KDQOL-36
Mapping
Issue Date2019
PublisherSpringer Verlag. The Journal's web site is located at http://link.springer.de/link/service/journals/10198/index.htm
Citation
The European Journal of Health Economics, 2019, Epub How to Cite?
AbstractObjectives: To develop algorithms mapping the Kidney Disease Quality of Life 36-Item Short Form Survey (KDQOL-36) onto the 3-level EQ-5D questionnaire (EQ-5D-3L) and the 5-level EQ-5D questionnaire (EQ-5D-5L) for patients with end-stage renal disease requiring dialysis. Methods: We used data from a cross-sectional study in Europe (France, n = 299; Germany, n = 413; Italy, n = 278; Spain, n = 225) to map onto EQ-5D-3L and data from a cross-sectional study in Singapore (n = 163) to map onto EQ-5D-5L. Direct mapping using linear regression, mixture beta regression and adjusted limited dependent variable mixture models (ALDVMMs) and response mapping using seemingly unrelated ordered probit models were performed. The KDQOL-36 subscale scores, i.e., physical component summary (PCS), mental component summary (MCS), three disease-specific subscales or their average, i.e., kidney disease component summary (KDCS), and age and sex were included as the explanatory variables. Predictive performance was assessed by mean absolute error (MAE) and root mean square error (RMSE) using 10-fold cross-validation. Results: Mixture models outperformed linear regression and response mapping. When mapping to EQ-5D-3L, the ALDVMM model was the best-performing one for France, Germany and Spain while beta regression was best for Italy. When mapping to EQ-5D-5L, the ALDVMM model also demonstrated the best predictive performance. Generally, models using KDQOL-36 subscale scores showed better fit than using the KDCS. Conclusions: This study adds to the growing literature suggesting the better performance of the mixture models in modelling EQ-5D and produces algorithms to map the KDQOL-36 onto EQ-5D-3L (for France, Germany, Italy, and Spain) and EQ-5D-5L (for Singapore).
Persistent Identifierhttp://hdl.handle.net/10722/273391
ISSN
2017 Impact Factor: 2.601
2015 SCImago Journal Rankings: 0.804

 

DC FieldValueLanguage
dc.contributor.authorYang, F-
dc.contributor.authorWong, CKH-
dc.contributor.authorLuo, N-
dc.contributor.authorPiercy, J-
dc.contributor.authorMoon, R-
dc.contributor.authorJackson, J-
dc.date.accessioned2019-08-06T09:28:03Z-
dc.date.available2019-08-06T09:28:03Z-
dc.date.issued2019-
dc.identifier.citationThe European Journal of Health Economics, 2019, Epub-
dc.identifier.issn1618-7598-
dc.identifier.urihttp://hdl.handle.net/10722/273391-
dc.description.abstractObjectives: To develop algorithms mapping the Kidney Disease Quality of Life 36-Item Short Form Survey (KDQOL-36) onto the 3-level EQ-5D questionnaire (EQ-5D-3L) and the 5-level EQ-5D questionnaire (EQ-5D-5L) for patients with end-stage renal disease requiring dialysis. Methods: We used data from a cross-sectional study in Europe (France, n = 299; Germany, n = 413; Italy, n = 278; Spain, n = 225) to map onto EQ-5D-3L and data from a cross-sectional study in Singapore (n = 163) to map onto EQ-5D-5L. Direct mapping using linear regression, mixture beta regression and adjusted limited dependent variable mixture models (ALDVMMs) and response mapping using seemingly unrelated ordered probit models were performed. The KDQOL-36 subscale scores, i.e., physical component summary (PCS), mental component summary (MCS), three disease-specific subscales or their average, i.e., kidney disease component summary (KDCS), and age and sex were included as the explanatory variables. Predictive performance was assessed by mean absolute error (MAE) and root mean square error (RMSE) using 10-fold cross-validation. Results: Mixture models outperformed linear regression and response mapping. When mapping to EQ-5D-3L, the ALDVMM model was the best-performing one for France, Germany and Spain while beta regression was best for Italy. When mapping to EQ-5D-5L, the ALDVMM model also demonstrated the best predictive performance. Generally, models using KDQOL-36 subscale scores showed better fit than using the KDCS. Conclusions: This study adds to the growing literature suggesting the better performance of the mixture models in modelling EQ-5D and produces algorithms to map the KDQOL-36 onto EQ-5D-3L (for France, Germany, Italy, and Spain) and EQ-5D-5L (for Singapore).-
dc.languageeng-
dc.publisherSpringer Verlag. The Journal's web site is located at http://link.springer.de/link/service/journals/10198/index.htm-
dc.relation.ispartofThe European Journal of Health Economics-
dc.rightsThis is a post-peer-review, pre-copyedit version of an article published in [The European Journal of Health Economics]. The final authenticated version is available online at: http://dx.doi.org/10.1007/s10198-019-01088-5-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectDialysis-
dc.subjectEQ-5D-3L-
dc.subjectEQ-5D-5L-
dc.subjectKDQOL-36-
dc.subjectMapping-
dc.titleMapping the kidney disease quality of life 36-item short form survey (KDQOL-36) to the EQ-5D-3L and the EQ-5D-5L in patients undergoing dialysis-
dc.typeArticle-
dc.identifier.emailWong, CKH: carlosho@hku.hk-
dc.identifier.authorityWong, CKH=rp01931-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1007/s10198-019-01088-5-
dc.identifier.pmid31338698-
dc.identifier.scopuseid_2-s2.0-85069445274-
dc.identifier.hkuros299951-
dc.publisher.placeGermany-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats