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

项目总结 这份独立之路奖的申请是由一位致力于改善 热带传染病群随机试验(CRTS)设计与分析。在世界各地，数以百计的 每年进行CRT，以评估针对传染病的新干预措施的效果， 尤其是在经历登革热、埃博拉、疟疾和其他传染病的热带发展中国家 疫情爆发。这些CRT的科学严谨性依赖于有效的统计分析方法， 解决CRT设计中的复杂性。然而，设计复杂和新颖的CRT的出现 已经超过了用于数据分析的因果推理方法的发展。这一缺口代表着一把钥匙 提供有效的样本量计算、有效估计和正确解释的障碍 干预效果评估。这项研究的总体目标是通过开发 有效、稳健和高效的统计方法。具体地说，申请人将解决统计方面的挑战 三种CRT设计中的一种：(1)协变量自适应随机化，它已被广泛用于减少 基线不平衡，(2)测试阴性设计，这是近年来越来越流行的 实现成本效益，以及(3)多臂阶梯楔形设计，有改进潜力 未来CRT的灵活性和高效性。在K99阶段，申请者将延长经验过程 在CRT中处理协变量自适应随机化的理论并提供理论和计算 对当前统计模型的评价。在R00阶段的第一年，申请者将专注于测试- CRT中的负面设计，并通过以下方式消除不同医疗寻求行为的偏差 表征潜在的因果关系图，并对自身不可诊断的症状进行推断。 最后，申请者将开发一种最佳设计，可以同时处理处理推出、多个 干预措施，以及各种结果类型。申请者将完成 指导传染病、统计学和生物统计学方面的知名研究人员，以确保他的过渡 R00阶段的终身教职和他作为一种主要传染病的出现 生物统计学家。在宾夕法尼亚大学，申请人享有丰富的内部课程资源， 与著名研究人员的研讨会、计算设备、合作和智力互动； 此外，申请人有机会获得外部培训机会，包括暑期学校、国家 会议，以及在肯尼亚进行审判时的实践学习。这些培训活动将推动研究 职业生涯，从而支持他实现学术独立，并最终领导一个 推进传染病研究的研究团队。

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.