Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/11284
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dc.contributor.authorYoung, TA-
dc.contributor.authorMukuria, C-
dc.contributor.authorRowen, D-
dc.contributor.authorBrazier, JE-
dc.contributor.authorLongworth, L-
dc.date.accessioned2015-08-25T14:33:57Z-
dc.date.available2015-05-21-
dc.date.available2015-08-25T14:33:57Z-
dc.date.issued2015-
dc.identifier.citationMedical Decision Making, 2015en_US
dc.identifier.issn0272989X15587497-
dc.identifier.issn1552-681X-
dc.identifier.urihttp://www.ncbi.nlm.nih.gov/pubmed/25997920-
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/11284-
dc.description.abstractBACKGROUND: Clinical trials in cancer frequently include cancer-specific measures of health but not preference-based measures such as the EQ-5D that are suitable for economic evaluation. Mapping functions have been developed to predict EQ-5D values from these measures, but there is considerable uncertainty about the most appropriate model to use, and many existing models are poor at predicting EQ-5D values. This study aims to investigate a range of potential models to develop mapping functions from 2 widely used cancer-specific measures (FACT-G and EORTC-QLQ-C30) and to identify the best model. METHODS: Mapping models are fitted to predict EQ-5D-3L values using ordinary least squares (OLS), tobit, 2-part models, splining, and to EQ-5D item-level responses using response mapping from the FACT-G and QLQ-C30. A variety of model specifications are estimated. Model performance and predictive ability are compared. Analysis is based on 530 patients with various cancers for the FACT-G and 771 patients with multiple myeloma, breast cancer, and lung cancer for the QLQ-C30. RESULTS: For FACT-G, OLS models most accurately predict mean EQ-5D values with the best predicting model using FACT-G items with similar results using tobit. Response mapping has low predictive ability. In contrast, for the QLQ-C30, response mapping has the most accurate predictions using QLQ-C30 dimensions. The QLQ-C30 has better predicted EQ-5D values across the range of possible values; however, few respondents in the FACT-G data set have low EQ-5D values, which reduces the accuracy at the severe end. CONCLUSIONS: OLS and tobit mapping functions perform well for both instruments. Response mapping gives the best model predictions for QLQ-C30. The generalizability of the FACT-G mapping function is limited to populations in moderate to good health.en_US
dc.languageENG-
dc.language.isoenen_US
dc.subjectEORTC-QLQ-C30en_US
dc.subjectEQ-5D-3Len_US
dc.subjectFACT-Gen_US
dc.subjectCanceren_US
dc.subjectHealth-related quality of lifeen_US
dc.subjectMapping functionsen_US
dc.titleMapping Functions in Health-Related Quality of Life: Mapping From Two Cancer-Specific Health-Related Quality-of-Life Instruments to EQ-5D-3L.en_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.1177/0272989X15587497-
dc.relation.isPartOfMed Decis Making-
Appears in Collections:Health Economics Research Group (HERG)

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