INCOMPLETE REPEATED-MEASURES DATA-ANALYSIS IN THE PRESENCE OF TREATMENT EFFECTS

This article illustrates an analysis strategy for repeated measures data where some of the measurements are missing from the subjects on one or more occasions, with a special application to data arising from repeated measurement designs when some observations are missing after the first treatment period. Using all available data, ''almost'' maximum likelihood estimators for the parameters of interest are obtained under the assumption of a multivariate normal distribution of the data. Inference procedures for these parameters in small samples from popular two-treatment designs are discussed.