Truncation is a statistical phenomenon that occurs in many time-to-event studies. For example, autopsy-confirmed studies of neurodegenerative diseases are subject to an inherent left and right truncation, also known as double truncation. When the goal is to study the effect of risk factors on survival, the standard Cox regression model cannot be used when the survival time is subject to truncation. Existing methods that adjust for both left and right truncation in the Cox regression model require independence between the survival times and truncation times, which may not be a reasonable assumption in practice. We propose an expectation-maximization algorithm to relax the independence assumption in the Cox regression model under left, right, or double truncation to an assumption of conditional independence on the observed covariates. The resulting regression coefficient estimators are consistent and asymptotically normal. We demonstrate through extensive simulations that the proposed estimator has little bias and has a similar or lower mean-squared error compared to existing estimators. We implement our approach to assess the effect of occupation on survival in subjects with autopsy-confirmed Alzheimer's disease.

中文翻译：

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依赖截断下的 Cox 回归模型

截断是在许多事件发生时间研究中发生的统计现象。例如，经尸检证实的神经退行性疾病研究受制于固有的左右截断，也称为双截断。当目标是研究风险因素对生存的影响时，当生存时间被截断时，不能使用标准的 Cox 回归模型。在 Cox 回归模型中调整左右截断的现有方法需要生存时间和截断时间之间的独立性，这在实践中可能不是一个合理的假设。我们提出了一种期望最大化算法，以将 Cox 回归模型中在左、右或双截断下的独立性假设放宽为对观察到的协变量的条件独立性假设。得到的回归系数估计量是一致的且渐近正态的。我们通过广泛的模拟证明，与现有的估计器相比，所提出的估计器几乎没有偏差，并且具有相似或更低的均方误差。我们实施我们的方法来评估职业对尸检证实的阿尔茨海默病患者生存的影响。