{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T21:24:16Z","timestamp":1792790656831,"version":"4.5.0"},"reference-count":40,"publisher":"SAGE Publications","issue":"7","license":[{"start":{"date-parts":[[2015,5,21]],"date-time":"2015-05-21T00:00:00Z","timestamp":1432166400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Med Decis Making"],"published-print":{"date-parts":[[2015,10]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.1177\/0272989x15587497","type":"journal-article","created":{"date-parts":[[2015,5,21]],"date-time":"2015-05-21T22:45:46Z","timestamp":1432248346000},"page":"912-926","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":46,"title":["Mapping Functions in Health-Related Quality of Life"],"prefix":"10.1177","volume":"35","author":[{"given":"Tracey A.","family":"Young","sequence":"first","affiliation":[{"name":"School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK (TAY, CM, DR, JEB)"},{"name":"HERG, Brunel University, Uxbridge, Middlesex, UK (LL)"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Clara","family":"Mukuria","sequence":"additional","affiliation":[{"name":"School of Health and Related Research (ScHARR), University of Sheffield, Sheffield, UK 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