Statistical methods for censored and dependently truncated data

审查和相关截断数据的统计方法

• 批准号: 9175459
• 负责人: REBECCA A. BETENSKY
• 金额: $ 33.76万
• 依托单位: HARVARD SCHOOL OF PUBLIC HEALTH
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-07-01 至 2020-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9175459
• 关键词: Address; Adopted; Alzheimer' s Disease; Brain Neoplasms; Cohort Studies; Collection; Cox Models; Data; Data Analyses; Data Set; Dementia; Dependence; Dependency; Derivation procedure; Development; Disease; Evaluation; Event; Failure; Impaired cognition; Impairment; Investigation; Joints; Left; Link; Methods; Modeling; Neurologic; Observational Study; Probability; Public Health; Research; Risk; Sampling; Statistical Methods; Structure; Survival Analysis; Survivors; Testing; Time; Weight; base; density; disease diagnosis; experience; follow-up; hazard; interest; method development; nervous system disorder; novel; simulation; tau Proteins

Project Summary Left truncation arises frequently in observational cohort studies, in which subjects are sampled into substudies at some time during their follow-up, but the time origin of interest occurred prior to substudy sampling. For example, in the National Alzheimer's Coordinating Center (NACC) cumulative data set, many subjects experienced onset of cognitive impairment prior to their entry to the data set, and thus their time from impairment to Alzheimer's disease (AD) diagnosis is left truncated by their time to NACC entry. Standard methods of risk set adjustment can be used to adjust for this delayed entry, as long as the critical assumption of quasi-independence (i.e., factorization of the joint density over the observable region) between the entry time and time to AD diagnosis holds. However, this assumption often does not hold, and the simple adjusted analyses are biased. Truncated data, unlike purely censored data, enable identification of this requisite dependence due to joint observation of both the entry (truncation) time and the event time, and formal statistical tests are available. This proposal is motivated by our team's collective and extensive engagement in neurological disease studies, which display pervasive dependent truncation, and is supported by our expertise in survival analysis. This proposal adopts a range of analytical approaches to address dependent truncation that arises through any of several possible mechanisms. We accommodate unexplained dependence through inversion of transformation models and permutation null distributions, nonparametric bounds and estimation, and semi-parametric models, covariate-induced dependence through inverse probability weighting methods, and dependence that is induced by sequential truncating events through copula models. This project will establish a significantly enhanced collection of usable and robust methods for the analysis of dependently truncated data, which will strengthen the validity of research findings from studies of major public health problems, such as Alzheimer's disease. Each of our aims involves derivation of asymptotic results, extensive simulation, and application to our motivating neurologic disease studies.

项目摘要 左截断经常出现在观测队列研究中，其中对受试者进行了取样 在其随访期间的某个时间，但是感兴趣的时间发生在取样之前。为了 例如，在国家阿尔茨海默氏症协调中心（NACC）累积数据集中，许多科目 在进入数据集之前，认知障碍经历了经历的发作，因此他们的时间从 阿尔茨海默氏病（AD）诊断的损害因其进入NACC的时间而被截断。标准 只要关键的假设 进入进入之间的准独立性（即关节密度的分解） AD诊断的时间和时间存在。但是，这个假设通常不存在，并且调整了简单 分析是有偏见的。与纯粹审查的数据不同，截短的数据可以识别此必要的数据 由于对进入时间（截断）时间和事件时间的联合观察以及正式的依赖 可用统计测试。该提议是由我们团队集体和广泛参与的动机 神经疾病研究，显示出普遍的依赖截断，并得到我们的专业知识的支持 在生存分析中。该建议采用了一系列分析方法来解决依赖的截断 这是通过几种可能的机制中的任何一种。我们容纳无法解释的依赖 通过转化模型和置换零分布，非参数界限和 估计和半参数模型，协变量通过反概率诱导的依赖性 加权方法和通过顺序截断事件通过copula引起的依赖性 型号。该项目将为该项目建立可用且可靠的方法的大量收集 分析相关截断的数据，这将增强研究结果的有效性 主要的公共卫生问题，例如阿尔茨海默氏病。我们的每个目标都涉及渐近派的衍生 结果，广泛的模拟以及对我们激励神经系统疾病研究的应用。