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

基本信息

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
项目类别：
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

项目摘要

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.

项目概要此“独立之路奖”申请由一位致力于改善经济状况的统计学家提交。热带传染病整群随机试验（CRT）的设计和分析。在全球范围内，有数百每年进行一次 CRT，以评估针对传染病的新干预措施的效果，特别是在患有登革热、埃博拉、疟疾和其他传染病的热带发展中国家爆发。这些 CRT 的科学严谨性依赖于有效的统计分析方法，这些方法充分解决 CRT 设计的复杂性。然而，设计复杂新颖的CRT的出现已经超过了数据分析因果推理方法的发展。这个差距是一个关键提供有效样本量计算、有效估计和正确解释的障碍干预效果估计。这项研究的总体目标是通过开发来克服这一障碍有效、稳健且高效的统计方法。具体来说，申请人将解决统计挑战三种 CRT 设计：(1) 协变量自适应随机化，已广泛用于减少基线不平衡，（2）测试阴性设计，近年来越来越流行实现成本效益，以及（3）多臂阶梯楔形设计，有可能提高未来 CRT 的灵活性和效率。在K99阶段，申请人将延长经验流程处理 CRT 中协变量自适应随机化的理论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，申请人将重点测试- CRT 中的负面设计，并通过以下方式消除差异化医疗保健行为带来的偏差：描述潜在的因果图并对自我无法诊断的症状进行推断。最后，申请人将开发一种最佳设计，可以同时处理治疗推出、多项治疗干预措施和各种结果类型。申请人将完成以下研究目标传染病、统计学和生物统计学领域知名研究人员的指导，以确保他的过渡到 R00 阶段的终身教授职位以及他作为主要传染病的出现生物统计学家。在宾夕法尼亚大学，申请者享有丰富的内部课程资源，研讨会、计算设备、与著名研究人员的合作和智力互动；此外，申请人还有机会获得外部培训机会，包括暑期学院、国家培训会议以及在肯尼亚进行的试验中的实践学习。这些培训活动将推动研究应用程序的职业生涯，从而支持他实现学术独立并最终领导研究团队推进传染病研究。