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

摘要: 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 II rates. In this paper, we performed a comparative simulation based on two different applications kidney transplantation research. Our results showed that the propensity score-based inverse probability weighting (IPW) log-rank test proposed by Xie Liu (2005) can be recommended as first descriptive approach it provides adjusted curves has acceptable Even better performance was observed for Wald of parameter corresponding exposure variable multivariable-adjusted Cox model. This last result is primary interest exponentially increasing use methods literature.