- Title
- Mapping between HAQ-DI and EQ-5D-5L in a Chinese patient population
- Creator
- Patton, Thomas; Hu, Hao; Luan, Luan; Yang, Keqin; Li, Shu-Chuen
- Relation
- Quality of Life Research Vol. 27, Issue 11, p. 2815-2822
- Publisher Link
- http://dx.doi.org/10.1007/s11136-018-1925-1
- Publisher
- Springer
- Resource Type
- journal article
- Date
- 2018
- Description
- Objectives: In order to address the current deficiency of health utility evidence relevant for economic evaluations involving treatments for rheumatoid arthritis (RA) in the Chinese setting, this study aims to develop a mapping algorithm linking the Health Assessment Questionnaire (HAQ) and EQ-5D-5L in a Chinese population of patients with RA. Methods: An estimation sample was obtained from a cross-sectional study that collected HAQ, the pain Visual Analogue Scale, and EQ-5D-5L in RA patients in two tertiary referral hospitals in China. Mapping algorithms were derived in this study using two alternative regression methods: the beta regression and a multivariate ordered probit regression. The internal validity of the mapping algorithms was assessed in each case by calculating predictive performance using a bootstrapping procedure. Results: Of the several algorithms developed using these data, predictive performance was shown to be better when VAS pain was included as a predictor and when the multivariate ordered probit regression method was used, rather than the beta regression method. The algorithms developed were shown to be comparable, in terms of predictive performance, to existing mapping studies despite the small sample size of the estimation data. Conclusion: It is hoped that the availability of these algorithms will facilitate the development of cost-effectiveness studies evaluating RA treatments in the Chinese health care setting.
- Subject
- mapping; rheumatoid arthritis; cost effectiveness; health utilities
- Identifier
- http://hdl.handle.net/1959.13/1412299
- Identifier
- uon:36457
- Identifier
- ISSN:0962-9343
- Rights
- This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
- Language
- eng
- Full Text
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