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

均值和分位数的半参数自适应设计和统计推断

• 批准号: 2014951
• 负责人: Hulin Wu
• 金额: $ 10万
• 依托单位: The University of Texas Health Science Center at Houston
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2014951&HistoricalAwards=false
• 关键词: Semiparametric; Adaptive; Designs; Statistical; Inference

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.

适应性设计是一个重要的和活跃的研究领域，由临床试验的不同要求驱动。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，因此自适应设计的当前状态与数据丰富的环境不匹配。该项目旨在开发适应性设计的新理论和方法，以通过有效地整合大量的协变量信息而不会导致模型错误指定来简化临床试验。该项目的成功将使大量可用数据用于适应性设计，并使试验参与者避免不必要的不安全暴露。该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影响。PI将整合研究和教育，通过课程和演示，在学生，研究人员，医生和项目经理中促进适应性设计，让妇女和代表性不足的少数民族学生参与研究，并在少数民族服务机构做演讲。该项目将侧重于三个主要研究方向。首先，PI计划开发一个新的半参数协变量调整响应自适应（CARA）设计系列以及分析方法，这些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纳入许多协变量。第二，在许多领域，科学家对尾部分位数的兴趣超过了平均值。此外，分位数推断通常是临床试验中的次要分析，使研究人员和政策制定者能够检测分布中沿着可能最适合新治疗的点。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计划开发无缝II/III期CARA设计，并进行连续监测。假设检验和估计都将进行研究。因此，适应性随机化、适应性无缝设计和序贯监测的优势将在一项试验中结合起来。该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估的支持。

项目成果

Covariate‐adjusted response‐adaptive designs based on semiparametric approaches

基于半参数方法的协变量调整响应自适应设计

• DOI: 10.1111/biom.13849
• 发表时间: 2023
• 期刊: Biometrics
• 影响因子: 1.9
• 作者: Zhu, Hai;Zhu, Hongjian
• 通讯作者: Zhu, Hongjian

Estimation of hurst exponent for sequential monitoring of clinical trials with covariate adaptive randomization

协变量自适应随机化连续监测临床试验的赫斯特指数估计

• DOI: 10.1016/j.cct.2022.106887
• 发表时间: 2022
• 期刊: Contemporary Clinical Trials
• 影响因子: 2.2
• 作者: Yang, Yiping;Zhu, Hongjian;Lai, Dejian
• 通讯作者: Lai, Dejian

Seamless Clinical Trials with Doubly Adaptive Biased Coin Designs

• DOI: 10.51387/23-nejsds25
• 发表时间: 2023
• 期刊: The New England Journal of Statistics in Data Science
• 作者: Hongjian Zhu;Jun Yu;D. Lai;Li Wang