Statistical Methods in COVID-19/PASC Clinical Research

COVID-19/PASC 临床研究的统计方法

• 批准号: 10584243
• 负责人: Andrea S Foulkes
• 金额: $ 43.51万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-03-06 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10584243
• 关键词: 2019-nCoV; Address; Adrenal Cortex Hormones; Anxiety; Biological; Biological Markers; Body mass index; C-reactive protein; COVID-19; COVID-19 pandemic; Cessation of life; Characteristics; Clinical; Clinical Research; Communities; Computer software; Data; Data Set; Diabetes Mellitus; Disease; Electronic Health Record; Ethnic Origin; Event; Fatigue; Fever; Functional disorder; Housing; Individual; Inpatients; Intervention; Investments; Knowledge; Lead; Long COVID; Measures; Mediation; Mediator; Mental Depression; Methods; Microvascular Dysfunction; Organ; Outcome; Outpatients; Pathway interactions; Patients; Pharmacologic Substance; Physicians; Post-Acute Sequelae of SARS-CoV-2 Infection; Quality of life; Race; Recovery; Research; Resolution; Risk; Risk Factors; SARS-CoV-2 infection; Scientist; Severities; Shortness of Breath; Sleep Disorders; Smoking; Source; Statistical Methods; Structural Models; Subgroup; Symptoms; Time; Translations; United States National Institutes of Health; Viral; analytical tool; brain fog; clinical investigation; cohort; coronavirus disease; data harmonization; data resource; data streams; demographics; disorder subtype; expectation; food insecurity; gastrointestinal symptom; inflammatory marker; innovation; interest; lifestyle factors; marginalized population; mortality; novel; open source; patient subsets; persistent symptom; repository; sex; social determinants; social health determinants; supplemental oxygen; tool; virus genetics

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.

项目概要 该提案旨在开发、评估和传播新颖的统计工具，以严格调查 严重急性呼吸综合征的临床谱、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 (COVID-19) 以及 SARS 的急性后遗症 CoV-2 感染 (PASC)。鉴于 COVID-19 大流行的规模巨大、PASC 的潜在严重性、 可用和预期数据流的复杂性，以及生物学和临床知识的缺乏， 迫切需要新颖且强大的统计方法来解决最紧迫的 PASC 相关问题 研究问题。可以利用观测数据的先进统计方法来解决许多问题 这些问题；然而，适当方法的识别、严格应用和改进需要 对临床背景和可用方法的细微差别的深入了解。到 为此，我们提出统计创新和现有方法的新颖转化，以显着推进 PASC 临床研究，汇集了一支由深入研究的医师科学家和生物统计学家组成的团队 嵌入到 COVID-19 临床研究中，以实现以下具体目标： 目标 1：开发和评估 用于研究从 SARS-CoV-2 感染到感染的机制途径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介体的方法 以及事件结果的时间；目标 2：应用、评估和扩展边际结构模型作为框架 在时间范围内评估干预措施对 PASC 相关严重后果可能性的影响 不同的混杂因素、竞争性和半竞争性风险、区间审查和未观察到的疾病 亚型；目标 3：使用期望最大化，开发和应用用于正向未标记数据的方法 利用测量的协变量和患者水平结果信息的方法。拟议的 将使用地方和国家新兴观测和电子病历数据资源来应用方法。这些 当我们领导严格的临床过程时，统计创新将改变我们对 PASC 临床过程的理解 应用到多个前沿数据资源。