Effect of the number of categories, number of time-points, and sample size on the recovery of random-effect ordinal regression model parameters

Abstract

The random-effect ordinal regression model (REORM) is formulated as a special case of the hierarchical generalized linear model to analyze the effect of a cluster randomized trial on the ordinal responses measured at several occasions. The random-effect model takes the dependency between observations based on the same cluster or subject into account by introducing one or more random effects. The parameters in the REORM can be estimated using the computer program WinBUGS, which adopts Markov chain Monte Carlo (MCMC) algorithms. An analysis of real data is provided as an example. Simulations were conducted to examine parameter recovery based on the best-fitting model in the empirical example. The results show that increasing sample size improved the accuracy of the random-effect variance component recovery. Increasing the number of categories and sample sizes improved the accuracy of threshold recovery, and increasing the number of time-points improved the accuracy of regression parameter recovery.