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.

中文翻译：

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纵向序数数据的贝叶斯非线性分位数回归方法

具有顺序结果的纵向数据通常出现在临床和社会研究中，其中关注的目的通常是分位数曲线，而不是简单的参考范围。在本文中，我们考虑通过潜在变量对纵向序数数据进行贝叶斯非线性分位数回归。Gibbs算法中的有效Metropolis-Hastings用于模型拟合。仿真研究和实际数据示例进行了评估所提出的方法的性能。结果表明，该方法具有良好的效果。