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.

+More

confounders randomized clinical trials augmented inverse probability weighted complete case estimators treatmentspecific survival distribution observational studies robustness test statistics ordinary log rank test

Xiaofei Bai,Anastasios A. Tsiatis,.A log rank type test in observational survival studies with stratified sampling. 22 (2),280-298.

Xiaofei Bai,Anastasios A. Tsiatis,"A log rank type test in observational survival studies with stratified sampling" 22

Xiaofei Bai,Anastasios A. Tsiatis,"A log rank type test in observational survival studies with stratified sampling"