We propose methods for analyzing longitudinal data, obtained in clinical trials
and other applications with repeated measures of responses taken over time. Common
characteristics of longitudinal studies are correlated responses and observations taken
at unequal points in time. The first part of this dissertation examines the justification
of a block bootstrap procedure for the repeated measurement designs, which takes
into account the dependence structure of the data by resampling blocks of adjacent
observations rather than individual data points. In the case of dependent stationary
data, under regular conditions, the approximately studentized or standardized block
bootstrap possesses a higher order of accuracy. With longitudinal data, the second
part of this dissertation shows that the diagonal optimal weights for unbalanced
designs can be made to improve the efficiency of the estimators in terms of mean
squared error criterion. Simulation study is conducted for each of the longitudinal
designs. We will also analyze repeated measurement data set concerning nursing home
residents with multiple sclerosis, which is obtained from a large database termed the
minimum data set (MDS)
