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

用于比较测试处理与控制处理的交叉设计：最优性、效率和稳健性

• 批准号: 0304661
• 负责人: Min Yang
• 金额: $ 8.52万
• 依托单位: University of Nebraska-Lincoln
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-07-15 至 2006-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0304661&HistoricalAwards=false
• 关键词: Crossover; Designs; Comparing; Test; Treatments

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.

该项目的目的是找到最佳，高效或健壮的跨界设计，以将多种测试处理与对照处理进行比较。 A-Ovility和MV典型性被认为是最佳标准。将应用正交矩阵理论来简化相应的信息矩阵。将采用置换技术来确定最佳标准下可实现的下限。五个目标旨在实现这一目标。 （1）在传统模型下确定并构建最佳/高效设计； （2）在自我和混合结转效应模型下确定并构建最佳/高效设计； （3）识别和构建在各种模型下表现良好的强大设计； （4）提出了目标（1）至（3）的相应算法，并开发一个软件包，以促进研究结果的传播和广泛应用； （5）在内布拉斯加州林肯大学的高级实验设计课程中开发课程。分裂设计已被广泛用于各种领域，尤其是在临床试验中。尽管在所有处理都同样重要时，就存在大量有关识别和构建最佳/高效或健壮的跨界设计的研究，但是在将多种测试治疗与对照治疗进行比较时，有关此类设计的知识非常有限，并且需要紧急需要。目前，关于如何进行此类实验几乎没有适用的指导。该研究的结果预计将显着减少临床试验中所需的时间，金钱和患者数量。此外，预计这项研究将有助于FDA改善其跨界设计的准则。此外，用户友好的软件包可以帮助统计学家和非统计学家利用项目的研究结果，从而降低成本并加快新药开发。