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，在观察队列研究中，受试者被抽样入子研究 在他们随访期间的某个时间，但感兴趣的时间来源发生在亚研究抽样之前。为 例如，在国家阿尔茨海默氏症协调中心(NACC)的累积数据集中，许多受试者 在他们进入数据集之前经历过认知障碍，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 可以使用风险集调整的方法对这种延迟进入进行调整，只要关键假设 条目之间的准独立性(即，可观测区域上的联合密度的因式分解) 诊断AD的时间和时间保持不变。然而，这一假设往往不成立，而简单的调整 分析是有偏见的。截断数据与纯删节数据不同，能够识别这一必要条件 由于联合观察进入(截断)时间和事件时间而产生的依赖性，以及形式 统计测试是可用的。这项建议的动机是我们的团队集体和广泛参与 神经疾病研究，显示普遍依赖截断，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截断问题。 这是通过几种可能的机制中的任何一种产生的。我们容纳了无法解释的依赖 通过变换模型和排列零分布的反演，非参数界和 通过逆概率估计和半参数模型，协变量诱导的相关性 加权方法，以及由通过Copula的连续截断事件引起的相关性 模特们。该项目将建立一个显著增强的可用和健壮的方法集合 对独立截断数据的分析，这将加强从以下研究得出的研究结果的有效性 重大公共卫生问题，如阿尔茨海默氏症。我们的每个目标都涉及到渐近性的推导 结果，广泛的模拟，并应用于我们的激励性神经疾病研究。

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.