Mixed-effect models for longitudinal responses with different types of dropout: an application to the Leiden 85-plus study

Longitudinal studies on cognitive functioning in geriatric populations usually cover short follow-up times and may be influenced by different sources of selection: only a portion of the designed sample may agree to participate in the study, and only some of the participants may complete the study. Motivated by a real-life data example, we discuss a variance component model with two peculiar features. First, we account for differences in individual status when entering the study by defining a flexible association structure between baseline and subsequent responses, where individual characteristics influencing entrance and participation in the follow-up are jointly modelled. Second, since we may argue that death and non-participation could not be treated as equivalent reasons for dropout, we introduce a pattern mixture model that takes into account the information on the time spent in the study and the reasons for dropout. The model is applied to data on cognitive functioning from the Leiden study, and its performance is analysed through a large-scale simulation study.