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

基本信息

批准号：
10734551
负责人：
Lu Mao
金额：
$ 33.66万
依托单位：
UNIVERSITY OF WISCONSIN-MADISON
依托单位国家：
美国
项目类别：
财政年份：
2019
资助国家：
美国
起止时间：
2019-12-01 至 2028-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10734551
关键词：
Accounting Address Archives Biological Markers Cardiopulmonary Cardiovascular system Cessation of life Characteristics Chest Pain Clinical Trials Complex Congestive Heart Failure Data Development Event Future Goals Grant Health Heart failure Hospitalization Investigation Machine Learning Measures Methodological Studies Methodology Methods Modeling Modernization Myocardial Infarction Outcome Patients Probability Randomized Recording of previous events Recurrence Research Design Research Personnel Risk Assessment Risk Factors Sample Size Severities Statistical Methods Stroke Subgroup Techniques Testing Time Time trend Trees Work classification trees conditioning cost design experience flexibility follow-up improved indexing influenza virus vaccine life history markov model member mortality novel predictive modeling predictive tools random forest response risk prediction secondary analysis semiparametric statistics theories tool treatment effect trial design user-friendly

项目摘要

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.

项目摘要：现代心血管（CV）试验经常收集有关多种致命和非致命事件的数据（例如，心脏失败，心脏病发作，中风，胸痛等）对患者的健康有不同的影响。 HABE开始出现新方法，试图捕获更多事件但是，患者的第一个事件。其组件的重要性（例如，死亡与简历住院）并不容易。足够的工具来测量治疗效果，设计未来的试验，评估风险因素并建立预测模型。在这个项目中，我们通过AIM 1中的四个特定目标解决了这些差距。非参数估计是虽然成对比较（整体和亚组的）定义哪个组件可以轻易优先于另一个组件。概率中心审查权重（IPCW）和增强的反可能性加权（AIPW）技术为适应U静态估计器以纠正审查偏差并提高效率（从而降低试验成本）在AIM 2中使用患者数据既有后的机器化。复合终点的新支撑方法的样本量，例如有限的平均时间，有利于在AIM 3中，在固定和组序列设计下进行处理和较大的损失率。在较早的工作以上的工作之后，用于复合终点的半参数回归模型分数（PW）模型。通过扩展的传统PO多个事件，可容纳非比例的胜利分数严重性。关于分类树以进行顺序响应，我们开发了加权的Gini索引的时间融合版本和分解节点的两种方法，以及用于交叉验证修剪的广义一致性指数对结果事件的时间和严重程度进行核算。最近缔结的流感疫苗的次要分析有效停止心脏胸膜事件和心力衰竭（投资）试验（ClinicalTrials.gov：NCT02787044）。在综合R档案网络（cran.r-project.org）上纳入新的和现有的R包装中用于从业者的公众使用。