Bayesian Nonlinear Quantile Regression Approach for Longitudinal Ordinal Data

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.

Keywords

Ordinal longitudinal data / Bayesian approach / Quantile regression / MCMC / Metropolis–Hastings algorithm

Funding

the National Key Research and Development Plan(2016YFC0800100)

National Natural Science Foundation of China(11671374)