CIF:Small:A Systems Approach to Statistics for N-of-1 Experimental Trials

CIF:Small:N-of-1 实验性试验统计的系统方法

• 批准号: 2326498
• 负责人: Benjamin Recht
• 金额: $ 59.96万
• 依托单位: University of California-Berkeley
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2326498&HistoricalAwards=false
• 关键词: CIF; Small; Systems; Approach; Statistics

Randomized experiments are arguably the most important contribution of twentieth-century statistics, providing a program to establish the value of interventions to populations of individuals. By randomly assigning individuals to treatment and control, statistical analysis can determine the magnitude of intervention effects and eliminate confounding explanations. However, randomized experiments yield conclusions about only populations, not individuals. When treatments have heterogeneous effects, a randomized experiment cannot inform an individual whether a treatment can work for them personally. In contrast, N-of-1 trials attempt to solve this problem by having a single individual apply random treatments in different time windows. If the effect is immediate and transient, standard tools from randomized experiments can be used to estimate the magnitude and significance of the effect. Yet, N-of-1 trials remain highly limited as there currently exists no general framework to understand effects that accumulate over long time periods or interventions that have a complex interaction with the individual. This project aims to devise a methodology of experiments for individuals, using ideas from engineering systems to build a sophisticated framework for understanding such complex N-of-1 experimental trials. It is anticipated that the work may eventually help caregivers find new treatments for their patients, minimize design time in robotic systems, provide new tools for those who suffer from addiction, and provide a framework for improving skills in educational settings or athletics.The work consists of three main thrusts. The first thrust casts the individual as a dynamical system and investigates the application of dynamical system-identification techniques in an experimental framework. The second thrust applies online-learning and adaptive-control tools to frame experiment designs as optimal-control problems, devising intervention plans over time to maximize outcomes. The third thrust studies how educators, coaches, therapists, and caregivers use their experience with former clients to quickly adapt to the specialized needs of new clients. The work conducted will draw on mathematical tools from machine learning, control theory, optimization, and signal processing.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.

随机实验可以说是20世纪统计学最重要的贡献，它提供了一个程序来确定干预对个体群体的价值。通过将个体随机分配到治疗组和对照组，统计分析可以确定干预效果的大小并消除混淆的解释。然而，随机实验只能得出关于群体的结论，而不是关于个体的结论。当治疗有不同的效果时，随机实验不能告诉个体治疗是否对他们个人有效。相比之下，N-of-1试验试图通过让单个个体在不同的时间窗中应用随机处理来解决这个问题。如果影响是即时和短暂的，可以使用随机实验的标准工具来估计影响的大小和显著性。然而，N-of-1试验仍然非常有限，因为目前还没有一般的框架来理解长期累积的影响或与个体有复杂相互作用的干预措施。该项目旨在为个人设计一种实验方法，利用工程系统的思想来构建一个复杂的框架，以理解这种复杂的N-of-1实验试验。预计这项工作最终可能会帮助护理人员为他们的病人找到新的治疗方法，最大限度地减少机器人系统的设计时间，为那些遭受成瘾的人提供新的工具，并为提高教育环境或运动技能提供一个框架。这项工作主要包括三个方面。第一个推力将个体视为一个动力系统，并在实验框架中研究动力系统识别技术的应用。第二个重点是应用在线学习和自适应控制工具，将实验设计框架为最优控制问题，随着时间的推移设计干预计划，以最大化结果。第三个重点是研究教育工作者、教练、治疗师和护理人员如何利用他们与前客户的经验来快速适应新客户的特殊需求。所进行的工作将利用机器学习、控制理论、优化和信号处理中的数学工具。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。