Testing for the presence of multiple sources of informative dropout in longitudinal data

Longitudinal studies tracking the rate of change are subject to patient dropout. This dropout process might not only be informative but also heterogeneous in the sense that different causes might contribute to multiple patterns of informative dropout. We propose a random-effects approach to test for homogeneity of informative dropout that accommodates the realistic situation where reasons for dropout are not fully understood, or perhaps are even entirely unknown. The proposed score test is robust in that it does not depend on the underlying distribution of the informative dropout random effects. The test allows for an additional level of clustering among participating subjects, as might be found in a family study, provided the informative dropout random effects have a known correlation structure.