Novel Statistical Methods for Complex Time-to-Event Data in Cardiovascular Clinical Trials

心血管临床试验中复杂事件发生时间数据的新统计方法

• 批准号: 10063907
• 负责人: Lu Mao
• 金额: $ 36.82万
• 依托单位: UNIVERSITY OF WISCONSIN-MADISON
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-12-01 至 2022-11-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10063907
• 关键词: Addendum; Address; Algorithms; Cardiopulmonary; Cardiovascular Diseases; Cardiovascular system; Cessation of life; Chest; Chronic; Chronic Disease; Clinical; Clinical Trials; Complex; Complication; Data; Data Coordinating Center; Development; Diagnostic; Dimensions; Disease; Dropout; Enrollment; Equation; Equine mule; Event; Foundations; Frequencies; Funding; Goals; Gold; Heart failure; Hospitalization; Incidence; Influenza; Investigation; Lead; Methodology; Methods; Modeling; Modernization; Myocardial Infarction; National Heart, Lung, and Blood Institute; Nature; North America; Outcome; Partner in relationship; Patients; Principal Investigator; Probability; Procedures; Process; Proportional Hazards Models; Publishing; Randomized; Randomized Controlled Trials; Recurrence; Research Personnel; Risk; Sampling; Seasons; Selection Bias; Statistical Methods; Stroke; Survival Analysis; Testing; Time; Universities; Vaccinated; Weight; Wisconsin; Work; arm; base; clinical trial analysis; clinically relevant; cohort; cost; design; dosage; follow-up; hazard; influenza virus vaccine; non-compliance; novel; prevent; primary endpoint; randomized trial; semiparametric; sound; survivorship; theories; tool; treatment arm; trial comparing; user-friendly

Project Summary: Many cardiovascular (CV) clinical trials feature complex composite outcomes consisting of multiple types of (possibly recurrent) events, e.g., heart failure, myocardial infarction, stroke, and death. In addition, due to the chronic nature of the disease, these long-term trials often suffer from non-randomized cohorts as a result of informative dropout, a complication that shakes the foundation of randomized controlled trials as the gold standard for clinical inquiry. Motivated by the INVESTED trial, an ongoing multi-season CV trial comparing two dosages of influenza vaccine (for which we serve as lead statisticians), this proposal aims to develop novel statistical methodology that is more robust, more efficient, and better suited for such long-term CV trials. This goal will be achieved via three specific aims. For specific aim 1, we tackle the problem of non-randomized cohort adjustment under a comprehensive framework of time-to-event analysis, including the well-known Kaplan-Meier curve, log-rank test, Cox regression model, and other methods for recurrent events and competing risks. We will develop a robust inverse probability of treatment weighting (IPTW) approach with non- /semi-parametrically estimated weights to correct for selection bias in non-randomized cohorts. For specific aim 2, we generalize the newly developed win-loss approach for composite outcomes from two-sample testing to the regression setting. The win-loss approach is targeted for composite endpoints consisting of prioritized components, e.g., death over non-fatal events. The information it extracts from multiple prioritized time-to- event outcomes is fuller, more interpretable, and clinically more relevant than that contained in time to the first event, the traditional target of analysis. For specific aim 3, we further generalize the win-loss approach to a nonparametric framework that allows the win-loss probabilities to depend on the follow-up time. Both generalizations of the win-loss approach will proceed in an estimand-driven way as recommended by the recently published ICH-E9(R1) Addendum. Statistical efficiency of the proposed procedures will be studied thoroughly using modern semiparametric and weak convergence theories. Development of efficient procedures will help minimize trial costs. User-friendly R packages that implement the algorithms of the proposed methods will be developed and disseminated through https://cran.r-project.org.

项目摘要： 许多心血管（CV）临床试验具有复杂的复合结果，包括多种类型 （可能经常发生的）事件，例如心力衰竭，心肌梗塞，中风和死亡。另外，由于 疾病的慢性性质，这些长期试验经常因由于 内容丰富的辍学，一种并发症，使随机对照试验的基础作为黄金的基础 临床查询的标准。受投资试验的激励 流感疫苗的剂量（我们是主要统计学家），该提议旨在发展新颖 统计方法更强大，更高效，更适合此类长期CV试验。这 目标将通过三个特定目标实现。对于特定目标1，我们解决了非随机问题的问题 在事件时间分析的全面框架下调整队列调整，包括众所周知 Kaplan-Meier曲线，对数秩检验，Cox回归模型以及其他复发事件和其他方法 竞争风险。我们将开发出一种强大的对治疗加权的逆可能性（IPTW）方法 /半参数估计的权重以校正非随机队列中的选择偏差。针对特定 AIM 2，我们概括了新开发的Win-loss方法，用于通过两样本测试的复合结果 回归设置。获胜方法的目标是由优先级组成的复合端点 组成部分，例如非致命事件的死亡。它从多个优先级的时间提取的信息 - 事件成果比第一个及时所包含的结果更饱满，更容易解释，并且在临床上更相关 事件，分析的传统目标。对于特定的目标3，我们进一步概括了一个失败的方法 非参数框架允许获胜概率取决于后续时间。两个都 获胜方法的概括将按照估计和驱动的方式按照 最近出版的ICH-E9（R1）附录。将研究提出程序的统计效率 彻底使用现代半参数和弱收敛理论。开发有效程序 将有助于最大程度地减少试用费用。用户友好的R软件包实现了所提出方法的算法 将通过https://cran.r-project.org开发和传播。