Repeated measures designs are commonly used in medical, sociological and behavioral researches.  There are several methods for analyzing data arising from such designs, but most of them assume parametric models for the data.  In many cases, the assumed parametric models are  either obviously violated or difficult to verify.

Available nonparametric methods are based on first-order asymptotics.  In this talk, second-order asymptotic results valid under general conditions will be presented.  It will be shown that neither distributional assumptions are needed beyond existence of first few moments, nor are assumptions on the covariance-structure required.

Unavoidable consequence of repeated measures design is the occurrence of missing data.  Extension of the second-order asymptotic results  to the case of missing observations will also be discussed.   Simulation results will be presented to show the finite sample performance of the results. Real data example from a smoking cessation trial will be used to illustrate the application of the results.