File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Improving the mapping of condition-specific health-related quality of life onto SF-6D score

TitleImproving the mapping of condition-specific health-related quality of life onto SF-6D score
Authors
KeywordsSF-6D
Quality of life
Mapping models
FACT-C
Cubic spline
Colorectal cancer
Issue Date2014
PublisherSpringer International Publishing. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0962-9343
Citation
Quality of Life Research, 2014, v. 23 n. 8, p. 2343-2353 How to Cite?
AbstractBackground This study sought to improve the predictive performance and goodness-of-fit of mapping models, as part of indirect valuation, by introducing cubic spline smoothing to map a group of health-related quality of life (HRQOL) measures onto a preference-based measure. Methods This study was a secondary analysis of a cross-sectional health survey data assessing the HRQOL for patients with colorectal neoplasms. Mapping functions of condition-specific functional assessment of cancer therapy—colorectal (FACT-C) onto preference-based SF-6D measure were developed using a dataset of 553 Chinese subjects with different stages of colorectal neoplasm. The missing values of FACT-C were imputed using multiple imputation. Then three widely applicable models (ordinary least square (OLS), Tobit and two-part models) were employed for the mapping function after applying the cubic spline smoothing on the data. For the evaluation of the effectiveness of cubic spline smoothing and multiple imputation, the goodness-of-fit and prediction performance of each model were compared. Results Analyses showed that the models fitted with transformed data from cubic spline smoothing offered better performance in goodness-of-fit and prediction than the models fitted with the original data. The values of $R^2$ were improved by over 10 %, and the root mean square error and the mean absolute error were both reduced. The best goodness-of-fit and performance were achieved by OLS model using transformed data from cubic spline smoothing. Conclusions Cubic spline smoothing and multiple imputation were recommended for the mapping of HRQOL measures onto the preference-based measure. Among the three mapping models, the simple-to-use OLS model had the best performance.
Persistent Identifierhttp://hdl.handle.net/10722/196388
ISSN
2015 Impact Factor: 2.429
2015 SCImago Journal Rankings: 1.158
ISI Accession Number ID
Grants

 

DC FieldValueLanguage
dc.contributor.authorYang, Yen_US
dc.contributor.authorWong, MYen_US
dc.contributor.authorLam, CLKen_US
dc.contributor.authorWong, CKHen_US
dc.date.accessioned2014-04-07T03:21:52Z-
dc.date.available2014-04-07T03:21:52Z-
dc.date.issued2014en_US
dc.identifier.citationQuality of Life Research, 2014, v. 23 n. 8, p. 2343-2353en_US
dc.identifier.issn0962-9343-
dc.identifier.urihttp://hdl.handle.net/10722/196388-
dc.description.abstractBackground This study sought to improve the predictive performance and goodness-of-fit of mapping models, as part of indirect valuation, by introducing cubic spline smoothing to map a group of health-related quality of life (HRQOL) measures onto a preference-based measure. Methods This study was a secondary analysis of a cross-sectional health survey data assessing the HRQOL for patients with colorectal neoplasms. Mapping functions of condition-specific functional assessment of cancer therapy—colorectal (FACT-C) onto preference-based SF-6D measure were developed using a dataset of 553 Chinese subjects with different stages of colorectal neoplasm. The missing values of FACT-C were imputed using multiple imputation. Then three widely applicable models (ordinary least square (OLS), Tobit and two-part models) were employed for the mapping function after applying the cubic spline smoothing on the data. For the evaluation of the effectiveness of cubic spline smoothing and multiple imputation, the goodness-of-fit and prediction performance of each model were compared. Results Analyses showed that the models fitted with transformed data from cubic spline smoothing offered better performance in goodness-of-fit and prediction than the models fitted with the original data. The values of $R^2$ were improved by over 10 %, and the root mean square error and the mean absolute error were both reduced. The best goodness-of-fit and performance were achieved by OLS model using transformed data from cubic spline smoothing. Conclusions Cubic spline smoothing and multiple imputation were recommended for the mapping of HRQOL measures onto the preference-based measure. Among the three mapping models, the simple-to-use OLS model had the best performance.en_US
dc.languageengen_US
dc.publisherSpringer International Publishing. The Journal's web site is located at http://springerlink.metapress.com/openurl.asp?genre=journal&issn=0962-9343en_US
dc.relation.ispartofQuality of Life Researchen_US
dc.rightsThe final publication is available at Springer via http://dx.doi.org/10.1007/s11136-014-0668-x-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectSF-6D-
dc.subjectQuality of life-
dc.subjectMapping models-
dc.subjectFACT-C-
dc.subjectCubic spline-
dc.subjectColorectal cancer-
dc.titleImproving the mapping of condition-specific health-related quality of life onto SF-6D scoreen_US
dc.typeArticleen_US
dc.identifier.emailLam, CLK: clklam@hku.hken_US
dc.identifier.emailWong, CKH: carlosho@hku.hken_US
dc.identifier.authorityLam, CLK=rp00350en_US
dc.description.naturepostprint-
dc.identifier.doi10.1007/s11136-014-0668-xen_US
dc.identifier.pmid24682669-
dc.identifier.scopuseid_2-s2.0-84909579449-
dc.identifier.hkuros228494en_US
dc.identifier.isiWOS:000341820000019-
dc.publisher.placeSwitzerlanden_US
dc.relation.projectA Study on Health-related Quality of Life of patients with Colorectal Neoplasm and Cost-Effectiveness Analysis of Colorectal Cancer Screening in Hong Kong-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats