In the marginal analysis of clustered data, two types of informativeness have been shown to bias standard method for marginal inference: informative cluster size, in which the number of observations in a cluster is associated with a response variable, and subcluster covariate informativeness, in which the probability that a covariate takes a certain value is associated with the response. Monte Carlo-based within-cluster resampling estimators and cluster- and covariate-weighted analytic estimators have been suggested to adjust for both of these problems. In this talk, we suggesting a unifying cluster-weighting paradigm for the marginal analysis of clustered data. We then apply this paradigm to unpaired, clustered data - data which are paired at the cluster level, but unpaired within cluster - and develop marginal correlation estimators for such data. The suggested estimators are evaluated through simulations studies, and illustrated with an application to a data from a longitudinal dental study.
Marginal correlation measures for unpaired clustered data under cluster-based informativeness
Date:
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Location:
University of Kentucky, Statistics Department MDS 223 Refresments: 3:30-4:00 Seminar: MDS 312
Speaker(s) / Presenter(s):
Doug Lorenz Assistant Professor, University of Louisville Department of Bioinformatics and BioStatistics
Event Series: