Semiparametric Adaptive Designs and Statistical Inference for Both the Mean and the Quantiles

适应性设计是临床试验多样化需求驱动下的一个重要而活跃的研究领域。然而，大多数自适应随机化设计要么没有很好地利用可用的协变量，要么依赖于不必要的模型假设，因此自适应设计的现状与数据丰富的环境不匹配。该项目寻求为适应性设计开发新的理论和方法，通过有效地整合大量的协变量信息来简化临床试验，而不会出现模型错误指定。该项目的成功将使大量可用的数据被用于适应性设计，并使试验参与者避免不必要的不安全暴露。这项研究将对一般实验设计及其在产品质量、食品工业、能源和建筑以及计算机模拟模型等领域的应用产生广泛的影响。PI将通过课程和演讲在学生、研究人员、医生和项目经理中促进适应性设计，让女性和代表不足的少数族裔学生参与研究，并在为少数族裔服务的机构发表演讲，从而将研究和教育结合起来。该项目将围绕三个主要研究方向展开。首先，PI计划开发一类新的半参数协变量调整响应自适应(CARA)设计和分析方法，这些设计和分析方法可以实现与效率和伦理相关的目标，并在没有模型错误指定的情况下合并许多协变量。其次，在许多领域，科学家对尾部分位数比平均值更感兴趣。此外，分位数推断通常是临床试验中的二次分析，使研究人员和政策制定者能够沿着分布找出可能最适合新疗法的点。PI计划开发一系列新的半参数CARA设计和分位数推断方法。第三，迫切需要降低开发成本，缩短新疗法的上市时间。PI计划开发具有顺序监测的无缝第二/第三阶段CARA设计。假设检验和估计都将被研究。因此，自适应随机化、自适应无缝设计和顺序监测的优势将结合在一项试验中。将探索渐近和有限样本属性，并将提供临床试验指导。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Adaptive design is an important and active research area driven by diverse requirements of clinical trials. However, most adaptive randomization designs either do not make good use of the available covariates or depend on unnecessary model assumptions, so the current state of adaptive designs does not match the data-rich environment. This project seeks to develop new theory and methodology for adaptive designs to streamline clinical trials by efficiently incorporating a vast amount of covariate information without model misspecification. The success of the project will allow a large quantity of available data to be utilized in adaptive designs, and trial participants to avoid unnecessary unsafe exposure. The research will have broad impacts on general experimental designs and their applications in fields such as product quality, food industry, energy and architecture, and computer simulation models. The PI will integrate research and education by promoting the adaptive designs among students, researchers, physicians, and project managers through courses and presentations, involving women and underrepresented minority students in research, and making presentations at minority-serving institutions. The project will focus on three main research directions. First, the PI plans to develop a new family of semiparametric covariate-adjusted response-adaptive (CARA) designs as well as analysis approaches that can achieve the objectives related to efficiency and ethics and incorporate many covariates without model misspecification. Second, in many fields, scientists are more interested in the tail quantiles than the mean. In addition, quantile inference is often a secondary analysis in clinical trials, allowing researchers and policymakers to detect the points along the distribution that may be the most amenable to the new treatment. The PI plans to develop a new family of semiparametric CARA designs and methods for quantile inference. Third, there is an urgent need to reduce development costs and shorten the time-to-market of new therapies. The PI plans to develop seamless phase II/III CARA designs with sequential monitoring. Both hypothesis testing and estimation will be investigated. Thus, the advantages of adaptive randomization, adaptive seamless design, and sequential monitoring will be combined in a single trial. Both asymptotic and finite-sample properties will be explored, and guidance for clinical trials will be offered.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.