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

项目总结： 现代心血管(CV)试验通常收集关于一系列致命和非致命事件(例如心脏)的数据 失败、心脏病发作、中风、胸痛等)对病人的健康有不同的影响。近年来， 新的方法已经开始出现，它们试图捕获比传统端点更多的事件 病人的第一件事。但是，为了说明复合终结点的总体，同时区分 其组成部分的重要性(例如，死亡与心血管住院)并非易事。就目前情况来看，调查人员仍然缺乏 有足够的工具来衡量治疗效果、设计未来试验、评估风险因素和建立预测模型。 在这个项目中，我们通过四个具体目标来解决这些差距。在目标1中，我们考虑一类一般的 通过成对比较(包括总体和分组)定义的非参数效应大小估计法，见 使用分级比较规则，可以容易地将一个组件优先于另一个组件。与之相反 概率截尾加权(IPCW)和增强的逆概率加权(AIPW)技术是 适应U-统计量估计器，以纠正审查偏差并提高效率(从而减少试验 成本)使用随机前后的患者数据。在目标2中，我们开发例程来计算功率和 新提出的复合端点方法的样本量，例如支持的受限平均时间 固定序贯设计和分组序贯设计下的治疗和活着损失率。在目标3中，我们提出了小说 复合终点的半参数回归模型遵循了早期关于比例窗口的工作. 分数(PW)模型。特别地，广义半参数比例赔率(GSPO)模型 通过将传统PO模型扩展到具有有序的多个事件来适应非比例获胜分数 严肃性。在目标4中，我们将生存树作为一种预测工具从单变量端点扩展到复合端点。绘图 在有序响应的分类树上，我们开发了加权基尼指数的时间整合版本和 节点分裂的两种方法，以及交叉验证剪枝的广义一致性指数，从而 考虑到结果事件的时间和严重性。开发的方法将用于 新近结束的流感疫苗有效阻止心胸事件的二次分析 失代偿性心力衰竭(INSTED)试验(ClinicalTrials.gov：NCT02787044)。与此同时，他们将被 纳入全面R档案网络(CRAN.R-project t.org)上新的和现有的R-包 供从业者公开使用。

Project Summary: Modern cardiovascular (CV) trials often collect data on a wide array of fatal and nonfatal events (e.g., heart failure, heart attack, stroke, chest pain, and etc.) with different implications for patient health. In recent years, new methods have started to emerge which seek to capture more events than the traditional endpoint of each patient’s first event. However, to account for the totality of a composite endpoint while differentiating the importance of its components (e.g., death vs CV hospitalization) is not easy. As it stands, investigators still lack adequate tools to measure treatment effects, design future trials, assess risk factors, and build prediction models. In this project, we address these gaps via four specific aims. In Aim 1, we consider a general class of nonparametric effect-size estimands defined though pairwise comparison (both overall and subgroup-wise), in which one component can be readily prioritized over another using a hierarchical rule of comparison. The inverse probability censoring weighting (IPCW) and augmented inverse probability weighting (AIPW) techniques are adapted to U-statistic estimators to correct for censoring bias and to improve efficiency (and thus reduce trial cost) using patient data both pre- and post-randomization. In Aim 2, we develop routines to calculate power and sample size for newly proposed methods for composite endpoints, such as the restricted mean time in favor of treatment and while-alive loss rate, under both fixed and group sequential designs. In Aim 3, we propose novel semiparametric regression models for composite endpoints following earlier work on the proportional win- fractions (PW) model. In particular, the generalized semiparametric proportional odds (GSPO) model accommodates nonproportional win fractions by extending traditional PO models to multiple events with ordered severities. In Aim 4, we extend survival trees as a predictive tool from univariate to composite endpoints. Drawing on classification trees for ordinal response, we develop time-integrated versions of the weighted Gini index and twoing approach for node-splitting, and of a generalized concordance index for cross-validative pruning, thereby accounting for both the timing and severity of the outcome events. The methods developed will be used for secondary analyses of the recently concluded INfluenza Vaccine to Effectively Stop cardio-Thoracic Events and Decompensated heart failure (INVESTED) trial (ClinicalTrials.gov: NCT02787044). Meanwhile, they will be incorporated into new and existing R-packages on the Comprehensive R Archive Network (CRAN.R-project.org) for public use by practitioners.