Conditional Linear Mixed Models

The main advantage of longitudinal studies is that they can distinguish changes over time within individuals (longitudinal effects) from differences among subjects at the start of the study (cross-sectional effects). In observational studies, however, longitudinal changes need to be studied after correction for potential important cross-sectional differences between subjects. It will be shown that, in the context of linear mixed models, the estimation of longitudinal effects may be highly influenced by the assumptions about cross-sectional effects. Furthermore, aspects from conditional and mixture inference will be combined, yielding so-called conditional linear mixed models that allow estimation of longitudinal effects (average trends as well as subject-specific trends), independent of any cross-sectional assumptions. These models will be introduced and justified, and extensively illustrated in the analysis of longitudinal data from 680 participants in the Baltimore Longitudinal Study of Aging.