Simulation-based study comparing multiple imputation methods for non-monotone missing ordinal data in longitudinal settings

Abstract

The application of multiple imputation (MI) techniques as a preliminary step to handle missing values in data analysis is well established. The MI methods can be classified into two broad classes, the joint modeling and the fully conditional specification approaches. Their relative performance for longitudinal ordinal data setting is not well documented. This paper intends to ll this gap by conducting a large simulation study on the estimation of the parameters of a longitudinal proportional odds model. The two MI methods are also illustrated on a real dataset of quality of life in a cancer clinical trial.