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:10311488
- 负责人:
- 金额:$ 36.88万
- 依托单位:
- 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 2023-08-31
- 项目状态:已结题
- 来源:
- 关键词:AddendumAddressAlgorithmsCardiopulmonaryCardiovascular DiseasesCardiovascular systemCessation of lifeChestChronicChronic DiseaseClinicalClinical TrialsComplexComplicationDataData Coordinating CenterDevelopmentDimensionsDiseaseDropoutEnrollmentEquationEquine muleEventFoundationsFrequenciesFundingGoalsGoldHeart failureHospitalizationIncidenceInfluenzaInvestigationLeadMethodologyMethodsModelingModernizationMyocardial InfarctionNational Heart, Lung, and Blood InstituteNatureNorth AmericaOutcomePartner in relationshipPatientsPrincipal InvestigatorProbabilityProceduresProcessProportional Hazards ModelsPublishingRandomizedRandomized Controlled TrialsRecurrenceResearch PersonnelRiskSamplingSeasonsSelection BiasStatistical MethodsStrokeSurvival AnalysisTestingTimeUniversitiesVaccinatedWeightWisconsinWorkarmbaseclinical trial analysisclinically relevantcohortcostdesigndiagnostic tooldosagefollow-uphazardinfluenza virus vaccinenon-compliancenovelpreventprimary endpointrandomized trialsemiparametricsoundsurvivorshiptheoriestooltreatment armtrial comparinguser-friendly
项目摘要
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.
项目概要:
许多心血管(CV)临床试验的特征是复杂的复合结局,包括多种类型的
(可能反复发生的)事件,例如,心力衰竭、心肌梗塞、中风和死亡。另外由于受到
由于该疾病的慢性性质,这些长期试验通常由于以下原因而遭受非随机队列
信息丢失,一个动摇随机对照试验基础的并发症,
临床调查标准。受INVESTED试验的启发,一项正在进行的多季CV试验比较了两种
剂量的流感疫苗(我们担任首席统计员),这项建议旨在开发新的
更稳健、更有效、更适合此类长期CV试验的统计学方法。这
我们将通过三个具体目标来实现这一目标。对于具体目标1,我们解决了非随机化问题,
在事件发生时间分析的综合框架下进行队列调整,包括众所周知的
Kaplan-Meier曲线、对数秩检验、考克斯回归模型等方法,
竞争风险。我们将开发一种稳健的治疗加权逆概率(IPTW)方法,
/半参数估计的权重,以校正非随机化队列中的选择偏倚。为特定
目的2,我们推广了新发展的双样本测试组合结果的输赢方法
回归设置。输赢方法针对的是由优先级排序的
组件,例如,非致命性事件的死亡。它从多个优先级时间中提取的信息
事件结局比第一次事件发生的时间更全面、更可解释、更具有临床相关性
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
一般的输赢方法将继续在一个估计驱动的方式,建议由
ICH-E9(R1)附录。将研究拟议程序的统计效率
充分利用现代半参数和弱收敛理论。制定有效的程序
将有助于最大限度地降低审判成本。用户友好的R包,实现了所提出的方法的算法
将通过https://cran.r-project.org编写和传播。
项目成果
期刊论文数量(0)
专著数量(0)
科研奖励数量(0)
会议论文数量(0)
专利数量(0)
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{{ truncateString('Lu Mao', 18)}}的其他基金
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 - 财政年份:2019
- 资助金额:
$ 36.88万 - 项目类别:
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:
10734551 - 财政年份:2019
- 资助金额:
$ 36.88万 - 项目类别:
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