A beta regression approach to nonparametric longitudinal data classification in clinical trials.

Classification is an important topic in statistical analysis. For example, in applications involving clinical trials, an often seen objective is to determine whether or not novel medicines and treatments differ from existing standards of care. There are numerous methods and approaches in the literature for this problem when the endpoint of interest is normally distributed or can be approximated by an asymptotic Normal distribution, yet, the approaches when using a non-normally distributed endpoint are limited. This is especially true when these endpoints are correlated across time. In this dissertation, we investigated several techniques for use with longitudinal, repeated measures data where there is a special interest in adapting some recent results found in the literature on Beta regression. The proposed methods provided a nonparametric, with regard to the design endpoint, model that can be used in the repeated measures problem.