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

项目总结： 许多心血管(CV)临床试验的特点是复杂的复合结果，由多种类型的 (可能是反复发生的)事件，例如心力衰竭、心肌梗死、中风和死亡。此外，由于 由于疾病的慢性性质，这些长期试验经常受到非随机队列的影响，原因是 信息性退出，这一并发症动摇了作为黄金的随机对照试验的基础 临床问诊标准。在投资试验的激励下，正在进行的多季CV试验比较了 流感疫苗的剂量(我们是流感疫苗的首席统计员)，这项建议旨在开发新的 更稳健、更有效、更适合这类长期CV试验的统计方法。这 目标将通过三个具体目标来实现。对于具体目标1，我们解决了非随机化的问题 在事件间隔时间分析的全面框架下进行队列调整，包括众所周知的 复发事件的Kaplan-Meier曲线、对数等级检验、Cox回归模型和其他方法 相互竞争的风险。我们将开发一种稳健的逆治疗概率加权(IPTW)方法，该方法具有非 /半参数估计的权重，以校正非随机队列中的选择偏差。对于特定的 目标2，我们推广了新发展的双样本组合结果的输赢方法 设置为回归设置。输赢方法针对的是由优先级组成的复合终端 组成部分，例如，非致命事件造成的死亡。它从多个按优先级排序的时间间隔中提取的信息 与第一次事件结果相比，事件结果更完整、更可解释，临床相关性更强。 事件，传统的分析对象。对于特定的目标3，我们进一步将输赢方法推广到 非参数框架，允许输赢概率取决于后续时间。两者都有 对输赢方法的概括将以估计和驱动的方式进行 最近出版的ICH-E9(R1)附录。将研究拟议程序的统计效率 充分运用现代半参数和弱收敛理论。制定高效的程序 将有助于将审判成本降至最低。实现所提出方法的算法的用户友好的R包 将通过https://cran.r-project.org.开发和传播

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.