Although hierarchical correlated data are increasingly available and
are being used in evidence-based medical practices and health policy decision
making, there is a lack of information about the strengths and weaknesses of
the methods of analysis with such data. In this paper, we describe the use of
hierarchical data in a family study of alcohol abuse conducted in Edmonton, Canada, that attempted to determine whether alcohol abuse in probands is
associated with abuse in their first-degree relatives. We review three methods
of analyzing discrete hierarchical data to account for correlations among the
relatives. We conclude that the best analytic choice for typical correlated
discrete hierarchical data is by nonlinear mixed effects modeling using a
likelihood-based approach or multilevel (hierarchical) modeling using a quasilikelihood
approach, especially when dealing with heterogeneous patient data.
Cite this paper
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