Crossover Designs for Comparing Test Treatments with a Control Treatment: Optimality, Efficiency, and Robustness

点击翻译按钮获取中文摘要

The goal of this project is to find optimal, efficient, or robust crossover designs for comparing several test treatments with a control treatment. A-optimality and MV-optimality are considered as optimality criteria. Orthogonal matrix theory will be applied to simplify the corresponding information matrix. Permutation techniques will be employed to determine the corresponding achievable lower bounds under the optimality criteria. Five objectives are designed to accomplish this goal. (1) Identify and construct optimal/efficient designs under the traditional model; (2) Identify and construct optimal/efficient designs under the self and mixed carryover effects model; (3) Identify and construct robust designs that perform well under various models; (4) Propose corresponding algorithms for Objectives (1) through (3) and develop a software package to facilitate the dissemination and wide application of the research results; and(5) Develop curriculum in the University of Nebraska-Lincoln's advanced experimental design courses.Crossover designs have been widely used in a variety of fields, especially in clinical trials. Although there exists a great deal of research on identifying and constructing optimal/efficient or robust crossover designs when all treatments are equally important, knowledge on such designs when comparing several test treatments with a control treatment is extremely limited and urgently needed. At present, there is little applicable guidance on how to conduct such experiments. The results of this study, when applied, are expected to significantly reduce the time, money, and the number of patients needed in clinical trials. In addition, it is expected that this research will help the FDA improve its guidelines to crossover designs. Furthermore, the user-friendly software package can help both statisticians and non-statisticians to utilize research results from the project, thus reduce costs and speed up new drug development.