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

项目摘要 这条通往独立奖的道路是由一位致力于改善 热带传染病群集随机试验（CRT）的设计和分析。在全球范围内， 每年进行社区康复治疗，以评估新的传染病干预措施的效果， 特别是在经历登革热、埃博拉、疟疾和其他传染病的热带发展中国家， 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，具有复杂和新颖设计的CRT的出现 已经超过了数据分析的因果推理方法的发展。这个缺口代表了一把钥匙 提供有效样本量计算、有效估计和正确解释的障碍 干预效果评估。本研究的总体目标是通过开发 有效、稳健和高效的统计方法。具体而言，申请人将应对统计挑战 三种CRT设计：（1）协变量自适应随机化，已被广泛用于减少 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，和（3）多臂阶梯楔设计，这有可能改善 灵活性和效率的未来CRT。在K99阶段，申请人将扩展经验过程 理论来处理CRT中的协变量自适应随机化，并提供理论和计算 对现有统计模型的评估。在R 00阶段的第一年，申请人将专注于测试- CRT的负面设计，并通过以下方式消除差异性就医行为带来的偏见 表征潜在的因果图并对自我不可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，多个 干预和各种结果类型。申请人须完成 在传染病，统计学和生物统计学方面建立研究人员的导师，以确保他的过渡 在R 00阶段获得终身教职，并成为一种主要的传染病 生物统计学家在宾夕法尼亚大学，申请人享有丰富的课程内部资源， 研讨会，计算设备，合作，并与著名的研究人员的智力互动; 此外，申请人可以获得外部培训机会，包括暑期学院，国家 会议，并在肯尼亚的审判中进行实践学习。这些培训活动将推动研究 申请的职业生涯，从而支持他实现学术独立，并最终领导一个 研究小组，以推进传染病的研究。

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.