A log rank type test in observational survival studies with stratified sampling.
In randomized clinical trials, the log rank test is often used to test the null hypothesis of the equality of treatment-specific survival distributions. In observational studies, however, the ordinary log rank test is no longer guaranteed to be valid. In such studies we must be cautious about potential confounders; that is, the covariates that affect both the treatment assignment and the survival distribution. In this paper, two cases were considered: the first is when it is believed that all the potential confounders are captured in the primary database, and the second case where a substudy is conducted to capture additional confounding covariates. We generalize the augmented inverse probability weighted complete case estimators for treatment-specific survival distribution proposed in Bai et al. (Biometrics 69:830-839, 2013) and develop the log rank type test in both cases. The consistency and double robustness of the proposed test statistics are shown in simulation studies. These statistics are then applied to the data from the observational study that motivated this research.
['Bai|Xiaofei|X|', 'Tsiatis|Anastasios A|AA|']
D003198:Computer Simulation D003324:Coronary Artery Disease D006801:Humans D015233:Models, Statistical D064887:Observational Studies as Topic D011336:Probability D016016:Proportional Hazards Models D012494:Sampling Studies D016019:Survival Analysis
Stratified sampling Log rank test Observational study Cox proportional hazards model Survival analysis