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

基于协变量信息的自适应设计：新设计及其属性

• 批准号: 1209164
• 负责人: Feifang Hu
• 金额: $ 11万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-07-01 至 2014-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1209164&HistoricalAwards=false
• 关键词: Adaptive; Design; Based; upon; Covariate

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.

协变信息通常可用，并且在临床研究中通常起着至关重要的作用。 分层排列的块设计和经典的协变量自适应设计已被广泛用于平衡临床试验中的重要协变量。这两种设计都有一些严重的缺点。此外，文献中没有理论上的协变量自适应设计。在这个项目中，提出了两个新的自适应设计系列，并研究了它们的特性。第一个设计家族克服了分层排列的块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。提出了第二个设计家族，以更有效地检测治疗和协变量之间的相互作用。研究人员还介绍了一种新技术（称为“漂移条件”），以研究协变量自适应设计的渐近特性。该项目将生产新的顺序工具来解决许多实际问题。所提出的方法将应用于某些特定的应用程序。该项目的目的是根据协变量信息开发新的临床试验方法。 借助当今的先进技术，在顺序实验中收集有用的协变量信息变得越来越容易。例如，科学家已经确定了许多新的生物标志物，这些新生物标志物在过去几十年中可能与某些疾病有关。由于现在可以收集有关每个患者重要生物标志物（协变量信息）的信息，因此将有关协变量的信息纳入临床试验的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家族，并研究其特性。第一个设计家族克服了古典协变量自适应设计的缺点。提出了第二个设计家族，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，人们将能够在临床试验中应用新设计作为个性化医学。该研究项目将生产一些先进的统计工具，这些工具可以应用于许多领域，包括药物开发，医学研究，工业实验，经济学和金融。