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关键作用。 分层排列区组设计和经典的协变量自适应设计已被广泛应用于临床试验中重要协变量的平衡。这两种设计都有一些严重的缺点。此外，文献中没有协变量自适应设计的理论依据。在这个项目中，提出了两个新的自适应设计族，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，因此提供了更好的平衡。第二个设计家族的提出，以更有效地检测治疗和协变量之间的相互作用。此外，研究人员介绍了一种新的技术（称为“漂移条件”），以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。本计画的目标是发展新的临床试验方法，以协变量资讯为基础。 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，科学家们已经发现了许多可能与某些疾病有关的新生物标志物。由于现在能够收集每个患者的重要生物标志物（协变量信息）的信息，因此将协变量信息纳入临床试验设计变得越来越重要。研究人员将提出两个新的自适应设计族，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。第二个设计家族的提出，以更有效地检测治疗和协变量之间的相互作用。该项目完成后，人们将能够在个性化医疗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，可应用于许多领域，包括药物开发，医学研究，工业实验，经济和金融。

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.