Vakulenko-Lagun Bella, Qian Jing, Chiou Sy Han, Wang Nancy, Betensky Rebecca A
Department of Statistics, University of Haifa, Mount Carmel, Haifa, Israel.
Department of Biostatistics and Epidemiology, University of Massachusetts, Amherst, Massachusetts, USA.
Biometrics. 2022 Dec;78(4):1390-1401. doi: 10.1111/biom.13545. Epub 2021 Aug 22.
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.
观察性研究中常常存在延迟进入的情况，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，标准生存分析方法要求截断时间和事件时间之间具有准独立性。错误地假设准独立性可能会导致估计有偏差。我们解决了事件时间与其左截断时间之间的依赖性由共享协变量引起时生存分布的估计问题。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，若所有共享协变量都被测量，则可针对截断和依赖性进行调整。所提出的方法还考虑了右删失。我们在模拟中评估所提出的方法，进行敏感性分析，并提供实际应用指南。我们将我们的方法应用于中枢神经系统淋巴瘤研究的数据进行说明。所提出的方法在R包depLT中实现。
