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A common problem in the longitudinal data analysis is the missing values due to subject’s missed visits and loss to follow up. Although many novel statistical approaches have been developed to handle such data structures in recent years, few methods are available to provide robust inference in the presence of outlying observations. In this paper we propose two methods, t-distribution model and robust normal model, for robust inference with non-ignorable nonmonotonic missing data problems in longitudinal studies. These methods are conceptually simple and computationally straight forward. We also conduct simulation studies and use a real data example to demonstrate the performance of these methods.

Robust inference for longitudinal data analysis with non-ignorable and non-monotonic missing values