Statistical methods for censored and dependently truncated data

项目摘要 左截断经常出现在观察性队列研究中，其中受试者被抽样到子研究中 在随访期间的某个时间，但关注的时间来源发生在子研究采样之前。为 例如，在国家阿尔茨海默氏症协调中心（NACC）累积数据集中，许多受试者 在进入数据集之前经历了认知障碍的发作，因此他们从 阿尔茨海默病（AD）诊断的损害被他们进入NACC的时间截断。标准 风险集调整方法可用于调整这种延迟进入，只要关键假设 准独立性（即，可观察区域上的联合密度的因式分解）之间的条目 时间和时间到AD诊断成立。然而，这种假设往往不成立，简单的调整 分析有偏见。截断数据，不像纯粹的删失数据，使识别这一必要的 由于进入（截断）时间和事件时间的联合观测而引起的相关性，以及形式上的 有统计检验。这一提议的动机是我们团队的集体和广泛的参与， 神经系统疾病的研究，其中显示普遍依赖截断，并支持我们的专业知识 生存分析。该提案采用了一系列分析方法来解决相关截断问题 通过几种可能的机制中的任何一种产生。我们包容无法解释的依赖 通过变换模型和置换零分布的反演， 估计，半参数模型，通过逆概率协变量诱导的相关性 加权方法，以及通过copula序列截断事件引起的相关性 模型该项目将建立一个显著增强的可用和强大的方法集合， 分析相关截断数据，这将加强研究结果的有效性， 重大公共卫生问题，如阿尔茨海默病。我们的每一个目标都涉及到渐近的推导， 结果，广泛的模拟，并应用于我们的动机神经疾病的研究。

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.