CAREER: Use of Covariate Information in Adaptive Designs

职业：在自适应设计中使用协变量信息

• 批准号: 0349048
• 负责人: Feifang Hu
• 金额: $ 40万
• 依托单位: University of Virginia Main Campus
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-09-01 至 2010-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0349048&HistoricalAwards=false
• 关键词: CAREER; Use; Covariate; Information; Adaptive

AbstractAn adaptive design is a sequential design where the design points are chosen based on both previous design points and the outcomes at those design points. Covariate information is often available and usually very important in statistical inference. Very few adaptive designs described in the literature attempt to incorporate covariate information. This proposal is concerned with using covariate information in adaptive designs. The investigator proposes a new class of adaptive designs under some optimal criteria. The new designs (i) can incorporate covariate information; and (ii) exhibit certain desired properties when there is no covariate information. The investigator then studies the properties of the proposed designs when incorporating covariates. Further he investigates the relationship between efficiency of estimation and the total number of failures in experiments. Based on these results, the investigator develops a procedure to select a suitable adaptive design for a particular problem.In clinical trials, prognostic factors are often available and usually very important. How to incorporate prognostic factors (covariates) into trial design is a critical, and conceptually difficult, problem. Adaptive designs use data sequentially as it is collected, making use of it in deciding, for example, how to allocate future subjects between treatment groups or even whether or not future subjects are needed to achieve some accuracy objective. In this proposal, the investigator will thoroughly investigate using covariate information in adaptive designs. Upon completion of this project, a class of adaptive designs, which incorporates covariate information, will be available for clinical trials and other experiments. Applications exist in a wide variety of fields: industrial experiments, bioassay, and clinical trials. The full impact of these designs will be realized through dissemination in the literature, presentations, group working sessions, and interdisciplinary collaborations. The project will stimulate researchers and students to work in the area of adaptive designs, and should lead to numerous graduate and undergraduate level projects and these

自适应设计是一种序贯设计，其中设计点的选择是基于先前的设计点和这些设计点的结果。协变量信息在统计推断中经常是可用的并且通常是非常重要的。在文献中描述的适应性设计很少尝试纳入协变量信息。该建议涉及在自适应设计中使用协变量信息。研究者在某些最优准则下提出了一类新的自适应设计。新的设计（i）可以包含协变量信息;（ii）当没有协变量信息时，表现出某些期望的特性。然后，研究者在合并协变量时研究所提出的设计的性质。此外，他调查之间的关系效率的估计和总数的失败在实验中。基于这些结果，研究者开发了一个程序，以选择一个合适的自适应设计为特定的问题。在临床试验中，预后因素往往是可用的，通常是非常重要的。 如何将预后因素（协变量）纳入试验设计是一个关键的，概念上困难的问题。自适应设计在收集数据时按顺序使用数据，利用数据决定如何在治疗组之间分配未来受试者，甚至决定是否需要未来受试者来实现某些准确性目标。在本提案中，研究者将在自适应设计中使用协变量信息进行彻底研究。本计画完成后，将有一类包含协变数资讯的适应性设计可供临床试验及其他实验之用。应用领域广泛：工业实验、生物测定和临床试验。这些设计的全面影响将通过文献传播、演讲、小组工作会议和跨学科合作来实现。该项目将刺激研究人员和学生在自适应设计领域的工作，并应导致许多研究生和本科生水平的项目，这些