Rank Procedures for Repeated Measures with Missing Values

Abstract

This article presents a nonparametric approach to analyzing research designs that include repeated observations on the same set of individuals or units, such as longitudinal panel studies. The data collected from different individuals are generally assumed to be independent, while several observations from the same individual or unit may be dependent. The approach suggested in this study is nonparametric in the sense that only the distribution functions are used to define the treatment effects, and suggested procedures are extensions of well-established rank order methods. Missing observations are especially common in studies with repeated measures, and three strategies for addressing this problem are compared: complete case analysis, last observation carried forward, and complete set analysis. The authors demonstrate these methods with an analysis of age trends and sex differences in alcohol consumption in a sample of U.S. adolescents who completed questionnaires four times in the 7th through 10th grades.