Statistical Methods in COVID-19/PASC Clinical Research

项目摘要 这项建议旨在开发、评估和传播新的统计工具，以严格调查 严重急性呼吸综合征的临床谱系、生物学基础和社会决定因素 冠状病毒2(SARS-CoV-2)感染、冠状病毒病(新冠肺炎)和SARS急性后遗症- 冠状病毒2型感染(PASC)。鉴于新冠肺炎疫情的巨大规模，PASC的潜在严重性， 现有和预期数据流的复杂性，以及生物学和临床知识的匮乏， 迫切需要新的和稳健的统计方法来解决与PASC相关的最紧迫的问题 研究问题。可以利用观测数据的高级统计方法来处理许多 这些问题；然而，识别、严格应用和推进Apropos方法需要 对临床背景和可用方法的细微差别能力有深入的了解。至 为此，我们提出了统计创新和对现有方法的新颖翻译，以显著推进 PASC临床研究，汇集了一支由医生科学家和生物统计学家组成的团队，他们对 嵌入新冠肺炎临床研究以实现以下具体目标：目标1：开发和评估 SARS-CoV-2感染致病机制研究的因果中介分析框架 PASC和PASC恢复，包括适应时变和不均匀间隔的调解器的方法 目标2：应用、评估和扩展边际结构模型，以此作为框架 评估干预措施对PASC相关严重后果可能性的影响，在时间范围内- 不同的混杂、竞争和半竞争风险、间隔审查和未观察到的疾病 子类型；目标3：使用期望最大化，开发和应用正向未标记数据的方法 利用测量的协变量和关于患者水平结果的信息的方法。建议数 方法将使用地方和国家新出现的观测和EHR数据资源。这些 统计学创新将改变我们对PASC临床过程的理解，因为我们领导严谨的 应用于多个前沿数据资源。

Project Summary This proposal aims to develop, evaluate and disseminate novel statistical tools for rigorous investigation of the clinical spectrum, biological underpinnings and social determinants of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection, COronoVIrus Disease (COVID-19), and Post-Acute Sequelae of SARS- CoV-2 infection (PASC). Given the enormous scale of the COVID-19 pandemic, the potential severity of PASC, the complexity of available and anticipated data streams, and the paucity of biological and clinical knowledge, there is an urgent need for novel and robust statistical methods to address the most pressing PASC related research questions. Advanced statistical methods for observational data can be leveraged to address many of these questions; however, the identification, rigorous application and advancement of apropos methods requires sophisticated understanding of both the clinical context and the nuanced capabilities of available methods. To this end, we propose statistical innovations and novel translation of existing methods to significantly advance PASC clinical research, bringing together a team of physician-scientists and biostatisticians who are deeply embedded in COVID-19 clinical research to achieve the following specific aims: Aim 1: Develop and evaluate a causal mediation analysis framework for investigating the mechanistic pathways from SARS-CoV-2 infection to PASC and PASC recovery, including methods to accommodate time-varying and unevenly spaced mediators and time-to-event outcomes; Aim 2: Apply, evaluate, and extend marginal structural models as a framework to assess the impact of interventions on likelihood of PASC associated severe outcomes, in the context of time- varying confounding, competing and semi-competing risks, interval censoring and unobserved disease subtypes; Aim 3: Develop and apply methods for positive unlabeled data, using an expectation-maximization approach that leverages measured covariates and information on patient-level outcomes. The proposed methods will be applied using local and national emerging observational and EHR data resource. These statistical innovations will transform our understanding of the clinical course of PASC as we lead rigorous application to several leading-edge data resources.