Comparisons of the performance of different statistical tests for time‐to‐event analysis with confounding factors: practical illustrations in kidney transplantation

Confounding factors are commonly encountered in observational studies. Several confounder‐adjusted tests to compare survival between differently exposed subjects were proposed. However, only few studies have compared their performances regarding type I error rates, and no study exists evaluating their type II error rates. In this paper, we performed a comparative simulation study based on two different applications in kidney transplantation research. Our results showed that the propensity score‐based inverse probability weighting (IPW) log‐rank test proposed by Xie and Liu (2005) can be recommended as a first descriptive approach as it provides adjusted survival curves and has acceptable type I and II error rates. Even better performance was observed for the Wald test of the parameter corresponding to the exposure variable in a multivariable‐adjusted Cox model. This last result is of primary interest regarding the exponentially increasing use of propensity score‐based methods in the literature.