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.