Abstract
Longitudinal data with ordinal outcomes commonly arise in clinical and social studies, where the purpose of interest is usually quantile curves rather than a simple reference range. In this paper we consider Bayesian nonlinear quantile regression for longitudinal ordinal data through a latent variable. An efficient Metropolis–Hastings within Gibbs algorithm was developed for model fitting. Simulation studies and a real data example are conducted to assess the performance of the proposed method. Results show that the proposed approach performs well.
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The authors are grateful to the editor and the anonymous referees for their helpful comments and suggestions, which have helped us produce a substantially improved version.
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This work is supported in part by the National Key Research and Development Plan (No. 2016YFC0800100) and National Natural Science Foundation of China Grant 11671374 and 71631006.
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Yang, H., Chen, Z. & Zhang, W. Bayesian Nonlinear Quantile Regression Approach for Longitudinal Ordinal Data. Commun. Math. Stat. 7, 123–140 (2019). https://doi.org/10.1007/s40304-018-0148-7
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DOI: https://doi.org/10.1007/s40304-018-0148-7