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Abstract:
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Wilcoxon rank-sum test is widely used for comparing distributions of outcomes from two independent populations (groups). There have been many attempts to extend the rank-sum test to clustered data. However, comparison of group-specific marginal distributions may not be sufficient in presence of some potentially useful covariables that are observed in the study. In fact, not accounting for the effect of these covariates can lead to biased and misleading inference. The purpose of this presentation is twofold. First, we develop a method to estimate the covariate effects using rank-based weighted estimating equations that are appropriate when the intra-cluster group size is informative. Second, we construct an aligned rank-sum test based on the covariate adjusted outcomes. Asymptotic distributions of the R estimators and the test statistic are established. Through simulation studies, we show the importance of selecting proper weights in constructing the estimating equations when informativeness is present through the cluster or intra-cluster group sizes. We also demonstrate the superiority and the robustness of our method in comparison to regular parametric linear mixed models.
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