Efficient Designs for Scientific Studies with Repeated Measurements Data

利用重复测量数据进行科学研究的高效设计

• 批准号: 1809681
• 负责人: Sam Hedayat
• 金额: $ 12万
• 依托单位: University of Illinois at Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-07-01 至 2022-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1809681&HistoricalAwards=false
• 关键词: Efficient; Designs; Scientific; Studies; Repeated

The PI will study and explore scientific situations dealing with repeated measurements on all experimental units in space and or time. Such studies are recommended by FDA for pharmacokinetic and pharmacodynamics (PK/PD) evaluations in one or more phases of clinical trials or for bio-availability studies in making regulatory decision for generic drugs. Likewise, such experiments are also highly desirable for studies in epidemiology, social networks, and agricultural field experiments, including marketing, physical and virtual stores. The PI will develop ready-to-use statistical/computational soft wares which will be designed, tested, and freely distributed for dissemination of knowledge on repeated measurements studies. The research outcomes will serve as a platform to a broad class of multidisciplinary researchers and practitioners. Scientists in the fields of functional Magnetic Resonance Imaging (fMRI), pharmacy, medical devices, and generic medicine will be provided with highly useful tools to carry out their studies and make proper and prudent evidence based inference from the collected data under repeated measurements.Repeated measurements on the same experimental unit, also known as crossover designs, often produce residual/neighbor/correlated effects which need to be modeled and explored. The PI will focus on the scientific situations both in terms of data collection (design) and modeling which have not been adequately addressed and studied before. In the process, computational tools will be developed with thorough study of the theoretical issues. Sound innovative techniques will be developed for identifying cost-effective designs in order to ascertain optimal and nearly optimal estimation of the primary and, if needed, secondary parameters embedded in models under repeated observations.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.

PI将研究和探索处理所有实验单位在空间和/或时间上的重复测量的科学情况。FDA推荐此类研究用于临床试验的一个或多个阶段的药代动力学和药效学(PK/PD)评估，或用于仿制药的监管决策中的生物利用度研究。同样，此类实验也非常适用于流行病学、社会网络和农业领域的实验，包括营销、实体商店和虚拟商店。PI将开发现成的统计/计算软件，这些软件将被设计、测试和免费分发，以传播关于重复测量研究的知识。研究成果将成为广泛的多学科研究人员和从业者的平台。功能磁共振成像(FMRI)、制药、医疗器械和仿制药领域的科学家将被提供非常有用的工具来开展他们的研究，并在重复测量下从收集的数据中做出适当和谨慎的基于证据的推断。在同一实验单元上的重复测量，也称为交叉设计，通常会产生残差/邻近/相关效应，需要建模和探索。PI将侧重于数据收集(设计)和建模方面的科学情况，这些情况以前没有得到充分的处理和研究。在这一过程中，将在深入研究理论问题的基础上开发计算工具。将开发可靠的创新技术来确定具有成本效益的设计，以确定在重复观测下嵌入模型中的主要参数和次要参数(如果需要)的最佳和近乎最佳的估计。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。