Li Chenxi
Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI, 48824, USA.
Lifetime Data Anal. 2018 Jul;24(3):509-531. doi: 10.1007/s10985-017-9408-1. Epub 2017 Aug 28.
When observational data are used to compare treatment-specific survivals, regular two-sample tests, such as the log-rank test, need to be adjusted for the imbalance between treatments with respect to baseline covariate distributions. Besides, the standard assumption that survival time and censoring time are conditionally independent given the treatment, required for the regular two-sample tests, may not be realistic in observational studies. Moreover, treatment-specific hazards are often non-proportional, resulting in small power for the log-rank test. In this paper, we propose a set of adjusted weighted log-rank tests and their supremum versions by inverse probability of treatment and censoring weighting to compare treatment-specific survivals based on data from observational studies. These tests are proven to be asymptotically correct. Simulation studies show that with realistic sample sizes and censoring rates, the proposed tests have the desired Type I error probabilities and are more powerful than the adjusted log-rank test when the treatment-specific hazards differ in non-proportional ways. A real data example illustrates the practical utility of the new methods.
当使用观察性数据比较特定治疗的生存率时，常规的双样本检验，如对数秩检验，需要针对治疗组在基线协变量分布方面的不平衡进行调整。此外，常规双样本检验所要求的在给定治疗的情况下生存时间和删失时间条件独立的标准假设，在观察性研究中可能并不现实。而且，特定治疗的风险往往是非比例的，导致对数秩检验的功效较低。在本文中，我们基于观察性研究的数据，通过治疗和删失加权的逆概率，提出了一组调整后的加权对数秩检验及其上确界版本，以比较特定治疗的生存率。这些检验被证明是渐近正确的。模拟研究表明，在具有实际样本量和删失率的情况下，当特定治疗的风险以非比例方式不同时，所提出的检验具有所需的I型错误概率，并且比调整后的对数秩检验更具功效。一个实际数据示例说明了新方法的实际效用。
