Christopher W Noel1, Robert F Stephens2, Jie Susie Su3, Wei Xu3, Murray Krahn2, Eric Monteiro4, David P Goldstein1, Meredith Giuliani5, Aaron R Hansen6, John R de Almeida1. 1. Department of Otolaryngology-Head and Neck Surgery, Princess Margaret Cancer Centre-University Health Network, University of Toronto, Toronto, Ontario, Canada. 2. Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada. 3. Department of Biostatistics, University Health Network, Toronto, Ontario, Canada. 4. Department of Otolaryngology-Head and Neck Surgery, Sinai Health System, University of Toronto, Toronto, Ontario, Canada. 5. Department of Radiation Oncology, Princess Margaret Cancer Centre-University Health Network, University of Toronto, Toronto, Ontario, Canada. 6. Department of Medical Oncology, Princess Margaret Cancer Centre-University Health Network, University of Toronto, Toronto, Ontario, Canada.
Abstract
BACKGROUND: We sought to develop mapping functions that use EORTC responses to approximate health utility (HU) scores for patients with head and neck cancer (HNC). METHODS: In total, 209 outpatients with HNC completed the EORTC QLQ-C30 & QLQ-H&N35 (EORTC), EQ-5D-5L and the HUI-3. Results of the EORTC were mapped onto both EQ-5D-5L and HUI-3 scores using ordinary least squares regression and two-part models. RESULTS: The OLS model mapping EORTC onto the EQ-5D-5L performed best (adjusted R2 = .75, 10-fold cross-validation RMSE = 0.064, MAE 0.050). The HUI-3 model mapping onto EORTC through OLS was more limited (adjusted R2 = .5746, 10-fold cross cross-validation RMSE = 0.168, MAE 0.080). The EQ-5D-5L model was able to discriminate between certain clinical indices of disease severity on subgroup analysis. CONCLUSION: The EORTC to EQ-5D-5L mapping algorithm has good predictive validity and may enable researchers to translate EORTC scores into HU scores for head and neck patients with cancer.
BACKGROUND: We sought to develop mapping functions that use EORTC responses to approximate health utility (HU) scores for patients with head and neck cancer (HNC). METHODS: In total, 209 outpatients with HNC completed the EORTC QLQ-C30 & QLQ-H&N35 (EORTC), EQ-5D-5L and the HUI-3. Results of the EORTC were mapped onto both EQ-5D-5L and HUI-3 scores using ordinary least squares regression and two-part models. RESULTS: The OLS model mapping EORTC onto the EQ-5D-5L performed best (adjusted R2 = .75, 10-fold cross-validation RMSE = 0.064, MAE 0.050). The HUI-3 model mapping onto EORTC through OLS was more limited (adjusted R2 = .5746, 10-fold cross cross-validation RMSE = 0.168, MAE 0.080). The EQ-5D-5L model was able to discriminate between certain clinical indices of disease severity on subgroup analysis. CONCLUSION: The EORTC to EQ-5D-5L mapping algorithm has good predictive validity and may enable researchers to translate EORTC scores into HU scores for head and neck patients with cancer.
Keywords:
QALYs; cross walking; head and neck cancer (HNC); health state utility values (HSUVs); health-related quality of life (HRQoL); mapping algorithms; multi-attribute utility instruments (MAUIs); preference-based measures
Authors: Christopher W Noel; Sareh Keshavarzi; David Forner; Robert F Stephens; Erin Watson; Eric Monteiro; Ali Hosni; Aaron Hansen; David P Goldstein; John R de Almeida Journal: Otolaryngol Head Neck Surg Date: 2021-07-27 Impact factor: 5.591