Adaptive Design Based upon Covariate Information: New Designs and Their Properties

协变量信息通常是可用的，并且通常在临床研究中起着关键作用。分层排列区组设计和经典的协变量自适应设计在临床试验中被广泛用于平衡重要的协变量。这两种设计都有一些严重的缺陷。此外，文献中没有从理论上证明协变量适应性设计的合理性。在本课题中，提出了两类新的自适应设计族，并研究了它们的性质。第一类设计克服了分层置换区组设计和经典协变量自适应设计的缺点，从而提供了更好的平衡性。第二类设计被提出以更有效地检测处理和协变量之间的交互作用。研究者还引入了一种新的技术(称为“漂移条件”)来研究协变量自适应设计的渐近性质。这个项目将产生新的顺序工具来解决许多实际问题。这个项目的目标是开发基于协变量信息的临床试验的新方法。随着当今先进技术的发展，在序贯实验中收集有用的协变量信息变得越来越容易。例如，在过去的几十年里，科学家们已经确定了许多可能与某些疾病有关的新的生物标记物。由于人们现在能够收集关于每个患者的重要生物标记物的信息(协变量信息)，因此将关于协变量的信息合并到临床试验的设计中变得越来越重要。研究人员将提出两类新的自适应设计，并研究它们的性质。第一类设计克服了经典协变量自适应设计的缺点。第二类设计被提出以更有效地检测处理和协变量之间的交互作用。该项目完成后，人们将能够在个性化药物的临床试验中应用新的设计。该研究项目将产生一些先进的统计工具，可应用于药物开发、医学研究、工业实验、经济和金融等许多领域。

Covariate information is usually available and often plays a critical role in a clinical study. The stratified permuted block design and the classical covariate-adaptive designs have been widely employed to balance important covariates in clinical trials. Both designs have some serious drawbacks. In addition, there is no theoretical justification of the covariate-adaptive designs in the literature. In this project, two new families of adaptive designs are proposed and their properties are studied. The first family of designs overcomes the drawbacks of the stratified permuted block design and the classical covariate-adaptive design, and hence provides better balance. The second family of designs is proposed to detect the interaction between treatment and covariate more efficiently. Also the investigator introduces a new technique (called "drift conditions") to study the asymptotic properties of covariate-adaptive designs. This project will produce new sequential tools for solving many practical problems. The proposed methods will be applied to some specific applications.The objective of this project is to develop new methods for clinical trials based upon covariate information. With today's advanced technology, it becomes easier and easier to collect useful covariate information in sequential experiments. For example, scientists have identified many new biomarkers that may link to certain diseases over the past several decades. Since one is now able to collect information on important biomarkers (covariate information) of each patient, it becomes more and more important to incorporate information on covariates into the design of clinical trials. The investigator will propose two new families of adaptive designs and study their properties. The first family of designs overcomes the drawbacks of the classical covariate-adaptive designs. The second family of designs is proposed to detect the interaction between treatment and covariate more efficiently. Upon completion of this project, one will be able to apply new designs in clinical trials for personalized medicine. The research project will produce some advanced statistical tools, which may be applied in many fields including drug development, medical studies, industrial experiments, economics and finance.