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Abstract:
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Longitudinal studies are subject to patient dropout, which may lead to biased estimates of the rate of change if the dropout process is informative. Multiple patterns of informative dropout are plausible, in that different causes of dropout might contribute to different patterns. These multiple patterns can be dichotomized into two groups: those exhibiting quantitative interaction and qualitative interaction. We explore a test for qualitative interaction based on simultaneous confidence intervals. The test accommodates the realistic situation where reasons for dropout are not fully understood, or perhaps are even entirely unknown. It also allows for an additional level of clustering among participating subjects, as might be found in a family study. We apply these methods to a longitudinal study exploring rates of change in cognitive functioning for a group of Alzheimer's patients.
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