R-Estimation of Covariate Effects and Aligned Rank-Sum Tests for a Group Effect in Clustered Data

• Activity Number: 405 - Nonparametric Testing in Complex Data Settings
• Type: Contributed
• Date/Time: Tuesday, August 1, 2017 : 2:00 PM to 3:50 PM
• Sponsor: Section on Nonparametric Statistics
• Author(s): Sandipan Dutta* and Somnath Datta
• Companies: Duke University and University of Florida
• Keywords: Clustered data ; Informative Cluster Size ; Aligned residual ; Rank estimation ; Rank-sum test

Abstract

There have been many attempts to extend the Wilcoxon rank-sum test to clustered data. Recently, one such rank-sum test (Dutta & Datta, 2016, Biometrics 72, 432-40) was developed to compare the group specific marginal distributions of outcomes in clustered data where the conditional distributions of outcomes depend on the number of observations from that group in a given cluster. However, comparison of group-specific marginal distributions may not be sufficient in presence of some potentially useful covariables. In fact, not accounting for the effect of these covariates can lead to biased and misleading inference. We develop a method to estimate the covariate effects using rank-based weighted estimating equations when the intra-cluster group size is informative, and construct an aligned rank-sum test based on the covariate adjusted outcomes. Through simulation studies, we show the importance of selecting the proper weights for the estimating equations when the cluster or intra-cluster group sizes are informative, and demonstrate the superiority of our method in comparison to regular parametric linear mixed models in clustered data.We apply our method to a real-life school data.