Influence diagnostics for multivariate growth curve models

Research has shown that in mixed effect longitudinal models, influential observations can
have a large effect on the estimates of subject-specific parameters. Furthermore, they cannot
always be detected by the classical Cook’s distance due to potentially large betweensubject
variation. Thus, influential observations should be approached by conditioning on
the subjects. However, no rigorous approach has been developed for influential observation
detection for multivariate longitudinal mixed models where more than one response is
measured for each subject at each time point. We propose a multivariate conditional Cook’s
distance for this more general situation. Examples are given to illustrate how the influential
observation in one characteristic changes the effects of both characteristics.