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DC Field | Value | Language |
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dc.contributor.author | Young, TA | - |
dc.contributor.author | Mukuria, C | - |
dc.contributor.author | Rowen, D | - |
dc.contributor.author | Brazier, JE | - |
dc.contributor.author | Longworth, L | - |
dc.date.accessioned | 2015-08-25T14:33:57Z | - |
dc.date.available | 2015-05-21 | - |
dc.date.available | 2015-08-25T14:33:57Z | - |
dc.date.issued | 2015 | - |
dc.identifier.citation | Medical Decision Making, 2015 | en_US |
dc.identifier.issn | 0272989X15587497 | - |
dc.identifier.issn | 1552-681X | - |
dc.identifier.uri | http://www.ncbi.nlm.nih.gov/pubmed/25997920 | - |
dc.identifier.uri | http://bura.brunel.ac.uk/handle/2438/11284 | - |
dc.description.abstract | BACKGROUND: 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.language | ENG | - |
dc.language.iso | en | en_US |
dc.subject | EORTC-QLQ-C30 | en_US |
dc.subject | EQ-5D-3L | en_US |
dc.subject | FACT-G | en_US |
dc.subject | Cancer | en_US |
dc.subject | Health-related quality of life | en_US |
dc.subject | Mapping functions | en_US |
dc.title | Mapping 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.type | Article | en_US |
dc.identifier.doi | http://dx.doi.org/10.1177/0272989X15587497 | - |
dc.relation.isPartOf | Med Decis Making | - |
Appears in Collections: | Health Economics Research Group (HERG) |
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FullText.pdf | 866.44 kB | Adobe PDF | View/Open |
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