Mapping the EORTC-QLQ-C30 to the EQ-5D-3L: An assessment of existing and newly developed algorithms
| Autor: | Fionn Woodcock, Brett Doble, Stephen B. Fox, Ian Collins, Theresa Hayes, Madhu Singh, Gary Richardson, Lara Lipton, So-Young Moon, Mark Lucas, Andrew Fellowes, Huiling Xu, Heather Thorne, John J. McNeil, Paula Lorgelly, David M. Thomas, Paul A. James, Tomas John, Gail Risbridger, Gavin Wright, Raymond Snyder |
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| Rok vydání: | 2018 |
| Předmět: |
Adult
Male Computer science Cost-Benefit Analysis Health Status computer.software_genre Severity of Illness Index External validity 03 medical and health sciences 0302 clinical medicine EQ-5D Neoplasms Surveys and Questionnaires Mapping algorithm Humans Longitudinal Studies Prospective Studies 030212 general & internal medicine Statistical Mode Models Statistical 030503 health policy & services Health Policy Eortc qlq c30 External validation Reproducibility of Results Patient Preference Middle Aged Patient preference Quality-adjusted life year Female Quality-Adjusted Life Years Data mining 0305 other medical science computer Algorithms |
| Zdroj: | Medical Decision Making. 38(8) |
| ISSN: | 1552-681X 0272-989X |
| Popis: | Objectives: To assess the external validity of mapping algorithms for predicting EQ-5D-3L utility values from EORTC QLQ-C30 responses not previously validated and to assess whether statistical models not previously applied are better suited for mapping the EORTC QLQ-C30 to the EQ-5D-3L. Methods: 3,866 observations for 1,719 patients from a longitudinal study (Cancer 2015) were used to validate existing algorithms. Predictive accuracy was compared to previously validated algorithms using root mean squared error, mean absolute error across the EQ-5D-3L range and for ten tumour-type specific samples as well as using differences between estimated QALYs. Thirteen new algorithms were estimated using a subset of the Cancer 2015 data (3,203 observations for 1,419 patients) applying various linear, response mapping, beta and mixture models. Validation was performed using two datasets composed of patients with varying disease severity not used in the estimation and all available algorithms ranked on their performance. Results: None of the five existing algorithms offer an improvement in predictive accuracy over preferred algorithms from previous validation studies. Of the newly estimated algorithms, a two-part beta model performed the best across the validation criteria and in datasets composed of patients with different levels of disease severity. Validation results did, however, vary widely between the two datasets and the most accurate algorithm appears to depend on health state severity as the distribution of observed EQ-5D-3L values varies. Linear models performed better for patients in relatively good health, whereas beta, mixture and response mapping models performed better for patients in worse health. Conclusion: The most appropriate mapping algorithm to apply in practice may depend on the disease severity of the patient sample whose utility values are being predicted. |
| Databáze: | OpenAIRE |
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