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Keywords:

  • generalized estimating equations;
  • missing at random;
  • regression analysis;
  • social network model;
  • variance homogeneity

Abstract

U-statistics represent an important class of statistics arising from modeling quantities of interest defined by multi-subject responses such as the classic Mann–Whitney–Wilcoxon rank tests. However, classic applications of U-statistics are largely limited to univariate outcomes within a cross-sectional data setting. As longitudinal study designs become increasingly popular in today's research, it is imperative to generalize the classic theory of U-statistics to such a study setting to meet the challenges of modern clinical and translational research. In this article, we focus on applications of U-statistics to longitudinal study data. We first give a brief overview of U-statistics and then discuss how to apply this powerful class of statistics to model quantities of interest with a longitudinal data setting. In addition to generalizing U-statistics and associated inference theory to longitudinal data analysis, we also discuss a class of function response models (FRM) to bring the power of U-statistics to uncharted territory. We illustrate applications of generalized U-statistics and FRM with data from some real longitudinal studies. WIREs Comp Stat 2011 3 457–471 DOI: 10.1002/wics.178

For further resources related to this article, please visit the WIREs website.