A skew-normal random effects model for longitudinal ordinal categorical responses with missing data

Abstract

Missing values are common in longitudinal data studies. The missing data mechanism is termed non-ignorable (NI) if the probability of missingness depends on the non-response (missing) observations. This paper presents a model for the ordinal categorical longitudinal data with NI non-monotone missing values. We assumed two separate models for the response and missing procedure. The response is modeled as ordinal logistic, whereas the logistic binary model is considered for the missing process. We employ these models in the context of so-called shared-parameter models, where the outcome and missing data models are connected by a common set of random effects. It is commonly assumed that the random effect follows the normal distribution in longitudinal data with or without missing data. This can be extremely restrictive in practice, and it may result in misleading statistical inferences. In this paper, we instead adopt a more flexible alternative distribution which is called the skew-normal distribution. The methodology is illustrated through an application to Schizophrenia Collaborative Study data [19] and a simulation.

Keywords:

• shared parameter
• non-ignorable
• non-monotone
• missing data
• ordinal categorical responses
• skew-normal
• random effect

Acknowledgements

The authors thank the referees and the editor for their many constructive comments, which significantly improved the quality of this paper. We are also grateful to the Shahid Chamran University of Ahvaz for their financial support.