Mapping of the EORTC QLQ-C30 to EQ-5D-5L index in patients with lymphomas
- PDF / 913,966 Bytes
- 11 Pages / 595.276 x 790.866 pts Page_size
- 76 Downloads / 199 Views
ORIGINAL PAPER
Mapping of the EORTC QLQ‑C30 to EQ‑5D‑5L index in patients with lymphomas Richard Huan Xu1,2 · Eliza Lai Yi Wong1,2 · Jun Jin3 · Ying Dou3 · Dong Dong1,2,4 Received: 19 January 2020 / Accepted: 21 July 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract Objective The objective of this study was to develop algorithms to map the EORTC QLQ-C30 (QLQ-C30) onto EQ-5D-5L in a sample of patients with lymphomas. Methods An online nationwide survey of patients with lymphoma was carried out in China. Ordinary least squares (OLS), beta-based mixture, adjusted limited dependent variable mixture regression, and a Tobit regression model were used to develop the mapping algorithms. The QLQ-C30 subscales/items, their squared and interaction terms, and respondents’ demographic variables were used as independent variables. The root mean square error (RMSE), mean absolute error (MAE), and R-squared (R2) were estimated based on tenfold cross-validation to assess the predictive ability of the selected models. Results Data of 2222/4068 respondents who self-completed the online survey were elicited for analyses. The mean EQ5D-5L index score was 0.81 (SD 0.21, range − 0.81–1.0). 19.98% of respondents reported an index score at 1.0. In total, 72 models were generated based on four regression methods. According to the RMSE, MAE and R2, the OLS model including QLQ-C30 subscales, squared terms, interaction terms, and demographic variables showed the best fit for overall and the Non-Hodgkin’s lymphoma sample; for Hodgkin’s lymphoma, the ALDVMM with 1-component model, including QLQ-C30 subscales, squared terms, interaction terms, and demographic variables, showed a better fit than the other models. Conclusion The mapping algorithms enable the EQ-5D-5L index scores to be predicted by QLQ-C30 subscale/item scores with good precision in patients living with lymphomas. Keywords EORTC QLQ-C30 · EQ-5D-5L · Mapping algorithm · Lymphoma · China
Introduction
Electronic supplementary material The online version of this article (https://doi.org/10.1007/s10198-020-01220-w) contains supplementary material, which is available to authorized users. * Dong Dong [email protected] 1
The Jockey Club School of Public Health and Primary Care, Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
2
Centre for Health Systems and Policy Research, The Chinese University of Hong Kong, Hong Kong, China
3
Department of Sociology, School of Social Sciences, Tsinghua University, Beijing, China
4
Shenzhen Research Institute, The Chinese University of Hong Kong, Shenzhen, Guangdong, China
Cost-utility analysis (CUA) is strongly encouraged for use in health economic evaluation, with the outcomes assessed by quality-adjusted life years (QALYs). It is a measure that considers the effect of both quantity and quality of life (QoL) years [1], of which QoL is assessed by utility values that reflect the general public’s preference. Despite a wealth of instruments that might be used to evaluate
Data Loading...
