Improving the design and statistical analysis of cluster-randomized trials on tropical infectious diseases

改进热带传染病整群随机试验的设计和统计分析

• 批准号: 10570440
• 负责人: Bingkai Wang
• 金额: $ 9.36万
• 依托单位: UNIVERSITY OF PENNSYLVANIA
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-05-17 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10570440
• 关键词: Accounting; Address; Award; Biometry; Caring; Clinical Research; Cluster randomized trial; Collaborations; Communicable Diseases; Complex; Cost efficiency; Data; Data Analyses; Dedications; Dengue; Developing Countries; Development; Disease Outbreaks; Ebola; Equation; Equipment; Evaluation; Evaluation Studies; Faculty; Funding; Future; Goals; Graph; Health Care Seeking Behavior; Individual; Infectious Diseases Research; Influenza; Intervention; Intervention Studies; Kenya; Malaria; Meningitis; Mentors; Mentorship; Methodology; Methods; Modeling; Outcome; Pathway interactions; Pennsylvania; Phase; Positioning Attribute; Procedures; Process; Randomized; Research; Research Personnel; Resources; Sample Size; Statistical Data Interpretation; Statistical Methods; Statistical Models; Symptoms; Techniques; Testing; Training; Training Activity; Universities; Vaccination; Wolbachia; Work; arm; career; cluster randomized design; computerized tools; design; disorder control; experience; flexibility; hands-on learning; improved; innovation; intervention effect; novel; pathogen; pragmatic trial; randomized trial; recruit; simulation; statistics; summer institute; symposium; tenure track; theories; training opportunity; trial design; vector control

PROJECT SUMMARY This Pathway to Independence Award application is submitted by a statistician committed to improving the design and analysis of tropical infectious disease cluster-randomized trials (CRTs). Worldwide, hundreds of CRTs are carried out annually to evaluate the effect of new interventions against infectious diseases, especially in tropical developing countries experiencing dengue, Ebola, malaria, and other infectious disease outbreaks. The scientific rigor of these CRTs relies on valid statistical analysis methods that adequately address the complexity in the CRT designs. However, the emergence of CRTs with complex and novel designs has outpaced the development of causal inference methods for data analysis. This gap represents a key barrier to providing valid sample size calculation, efficient estimation, and correct interpretation of the intervention effect estimates. The overarching goal of this research is to surmount this barrier by developing valid, robust, and efficient statistical methods. Specifically, the applicant will address the statistical challenges of three CRT designs: (1) covariate-adaptive randomization, which has been extensively used for reducing baseline imbalance, (2) the test-negative design, which has been increasingly popular in recent years for achieving cost-efficiency, and (3) the multi-arm stepped-wedge design, which has the potential to improve flexibility and efficiency for future CRTs. In the K99 phase, the applicant will extend the empirical process theory to handle covariate-adaptive randomization in CRTs and provide both theoretical and computation evaluations of current statistical models. During the first year of the R00 phase, the applicant will focus on test- negative designs in CRTs and eliminate the bias from differential healthcare-seeking behavior by characterizing the underlying causal graph and performing inference on self-nondiagnosable symptoms. Finally, the applicant will develop an optimal design that can simultaneously handle treatment roll-out, multiple interventions, and various outcome types. The applicant will accomplish the research aims under the mentorship of established researchers in infectious disease, statistics, and biostatistics to assure his transition to a tenure-track faculty position in the R00 phase and his emergence as a leading infectious disease biostatistician. At the University of Pennsylvania, the applicant enjoys rich internal resources of courses, seminars, computational equipment, collaborations, and intellectual interactions with prestigious researchers; furthermore, the applicant has access to external training opportunities including summer institutes, national conferences, and hands-on learning in trial conduct in Kenya. These training activities will propel the research career of the application, thereby supporting his achieving academic independence and ultimately leading a research team to advance the research of infectious diseases.

项目摘要 这项通往独立奖申请的途径由致力于改进的统计学家提交 热带传染病聚集群集试验（CRT）的设计和分析。全球，数百个 每年进行CRT，以评估针对传染病的新干预措施的影响， 尤其是在热带发展中国家，经历了登革热，埃博拉病毒，疟疾和其他传染病 爆发。这些CRT的科学严谨性依赖于充分的有效统计分析方法 解决CRT设计中的复杂性。但是，具有复杂和新颖设计的CRT的出现 用于数据分析的因果推理方法的发展。此差距代表关键 提供有效的样本量计算，有效估计和正确解释的障碍 干预效应估计。这项研究的总体目标是通过开发来克服这一障碍 有效，健壮，有效的统计方法。具体而言，申请人将解决统计挑战 在三种CRT设计中：（1）协变量自适应随机化，已广泛用于还原 基线不平衡，（2）测试阴性设计，近年来越来越受欢迎 达到成本效率，以及（3）多臂阶梯式设计设计，有可能改善 未来CRT的灵活性和效率。在K99阶段，申请人将扩展经验过程 理论处理CRT中的协变量自适应随机化并提供理论和计算 当前统计模型的评估。在R00阶段的第一年，申请人将专注于测试 - CRT中的负面设计，消除了通过差异医疗保健行为的偏见 表征基本因果图并对自我诊断症状进行推断。 最后，申请人将开发一种最佳设计，可以同时处理治疗推出，多个 干预措施和各种结果类型。申请人将完成研究的目的 在传染病，统计和生物统计学领域的知名研究人员的指导，以确保他的过渡 在R00阶段担任统治轨道的职位，并成为主要的传染病 生物统计学家。在宾夕法尼亚大学，申请人享受丰富的内部资源课程， 研讨会，计算设备，合作以及与著名研究人员的智力互动； 此外，申请人可以访问包括夏季学院，国家机构在内的外部培训机会 肯尼亚的会议和动手学习。这些培训活动将推动研究 该应用程序的职业，从而支持他实现学术独立性，并最终领导 研究团队可以推进传染病的研究。