Rank-based regression with repeated measurements data

A rank-based regression method is proposed for repeated measurements data. It is a generalisation of the classical Wilcoxon-Mann-Whitney rank statistic for independent observations. The method is valid under a weak condition on the error terms that can accommodate certain heteroscedasticity and within-subject dependency. The asymptotic normality of the proposed estimator is proved using empirical process theory. A variance estimator, shown to be consistent, is also constructed. The proposed method is illustrated using data from a clinical trial on treating labour pain. Robustness and efficiency of the estimator is demonstrated in simulation studies.