Li Chenxi
Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, USA.
Stat Methods Med Res. 2019 Sep;28(9):2649-2664. doi: 10.1177/0962280218785926. Epub 2018 Jul 9.
The log-rank test is widely used to test difference in event time distribution between treatment groups. However, if subjects are not randomly assigned to treatment groups, which is often the case in observation studies, the log-rank test is not asymptotically correct for detecting group survival difference due to the imbalance of confounding variables between groups. We develop a class of modified weighted log-rank tests and Renyi-type tests for two-sample survival comparison under non-random treatment assignment. The new tests can also account for non-random censoring that depends on baseline covariates. The proposed methods involve building working models for treatment assignment, cause-specific hazard of dependent censoring, and the time to event. We prove that, when either the models for treatment assignment and dependent censoring or the model for the event time is true, the new tests are asymptotically correct, i.e. being doubly robust. Numerical experiments demonstrate the tests' double-robustness property in finite samples of realistic sizes, and also show that the doubly robust log-rank test is at least as powerful as the regular log-rank test when the treatment assignment is random and there is no dependent censoring. An application to a kidney transplant data set illustrates the utility of the proposed methods.
对数秩检验被广泛用于检验治疗组之间事件时间分布的差异。然而，如果受试者不是随机分配到治疗组，而这在观察性研究中经常出现，由于组间混杂变量的不平衡，对数秩检验在检测组间生存差异时渐近不正确。我们开发了一类修正的加权对数秩检验和Renyi型检验，用于在非随机治疗分配下的两样本生存比较。新的检验还可以考虑依赖于基线协变量的非随机删失。所提出的方法涉及构建治疗分配、依赖删失的特定病因风险以及事件发生时间的工作模型。我们证明，当治疗分配和依赖删失的模型或事件时间的模型正确时，新的检验是渐近正确的，即具有双重稳健性。数值实验证明了检验在实际大小的有限样本中的双重稳健性，并且还表明，当治疗分配是随机的且不存在依赖删失时，双重稳健对数秩检验至少与常规对数秩检验一样有效。对一个肾移植数据集的应用说明了所提出方法的实用性。
