There is often delayed entry into observational studies, which results in left truncation. In the estimation of the distribution of time-to-event from left-truncated data, standard survival analysis methods require quasi-independence between the truncation time and event time. Incorrectly assuming quasi-independence may lead to biased estimation. We address the problem of estimation of the survival distribution when dependence between the event time and its left truncation time is induced by shared covariates. We introduce propensity scores for truncated data and propose two inverse probability weighting methods that adjust for both truncation and dependence, if all of the shared covariates are measured. The proposed methods additionally allow for right censoring. We evaluate the proposed methods in simulations, conduct sensitivity analyses, and provide guidelines for use in practice. We illustrate our approach in application to data from a central nervous system lymphoma study. The proposed methods are implemented in the R package, depLT.

中文翻译：

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协变量相关截断下生存分布的非参数估计

通常会延迟进入观察性研究，这会导致左截断。在从左截断数据估计事件发生时间的分布时，标准生存分析方法需要截断时间和事件时间之间的准独立性。错误地假设准独立性可能会导致估计有偏。当事件时间与其左截断时间之间的相关性由共享协变量引起时，我们解决了生存分布估计的问题。我们引入了截断数据的倾向得分，并提出了两种逆概率加权方法，如果测量了所有共享协变量，则可以针对截断和依赖性进行调整。所提出的方法还允许进行权利审查。我们在模拟中评估所提出的方法，进行敏感性分析，并提供实际使用指南。我们说明了我们对中枢神经系统淋巴瘤研究数据的应用方法。所提出的方法在 R 包中实现，部门。