2019
DOI: 10.1371/journal.pone.0226077
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Mapping the EORTC QLQ-C30 and QLQ-H&N35 to the EQ-5D for head and neck cancer: Can disease-specific utilities be obtained?

Abstract: IntroductionInnovations in head and neck cancer (HNC) treatment are often subject to economic evaluation prior to their reimbursement and subsequent access for patients. Mapping functions facilitate economic evaluation of new treatments when the required utility data is absent, but quality of life data is available. The objective of this study is to develop a mapping function translating the EORTC QLQ-C30 to EQ-5D-derived utilities for HNC through regression modeling, and to explore the added value of disease-… Show more

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Cited by 22 publications
(18 citation statements)
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“…We believe our models are highly applicable achieving a good degree of precision, also in lower spectrums of health, while at the same time avoiding high complexity with a manageable number of predictors. Our results in terms of the nRMSE and nMAE perform very well compared to what is usually reported for mapping algorithms [17,[43][44][45][46][47].…”
Section: Summary Of Main Findingssupporting
confidence: 80%
“…We believe our models are highly applicable achieving a good degree of precision, also in lower spectrums of health, while at the same time avoiding high complexity with a manageable number of predictors. Our results in terms of the nRMSE and nMAE perform very well compared to what is usually reported for mapping algorithms [17,[43][44][45][46][47].…”
Section: Summary Of Main Findingssupporting
confidence: 80%
“…The EQ-5D index value can therefore now be predicted from the PROMIS-29 in three major European countries for use in economic evaluations of health interventions. Our results in terms of the RMSE and MAE are well within the limits of what is usually reported for mapping algorithms (13,(36)(37)(38)(39)(40). The global underestimation of the predicted EQ-5D values in OLS has also been reported in dialysis patients (41).…”
Section: Optimal Models For Predicting Health Utility In the Three Cosupporting
confidence: 89%
“…We believe our models are highly applicable achieving a good degree of precision, also in lower spectrums of health, while at the same time avoiding high complexity with a manageable number of predictors. Our results in terms of the nRMSE and nMAE perform very well compared to what is usually reported for mapping algorithms (17,(44)(45)(46)(47)(48).…”
Section: Summary Of Main Ndingssupporting
confidence: 80%