The Type I Error and Power of Non-Parametric Logrank and Wilcoxon Tests With Adjustment for Covariates--A Simulation Study

Stat Med. 2008 Dec 10;27(28):5850-60. doi: 10.1002/sim.3406.


Time-to-event outcomes are common for oncology clinical trials. Conventional methods of analysis for these endpoints include logrank or Wilcoxon tests for treatment group comparisons, Kaplan-Meier survival estimates, and Cox proportional hazards models to estimate the treatment group hazard ratio (both unadjusted and adjusted for relevant covariates). Adjusting for covariates reduces bias and may increase precision and power (Statist. Med. 2002; 21:2899-2908). However, the appropriateness of the Cox proportional hazards model depends on parametric assumptions. One way to address these issues is to use non-parametric analysis of covariance (J. Biopharm. Statist. 1999; 9:307-338). Here, we carry out simulations to investigate the type I error and power of the unadjusted and covariate-adjusted non-parametric logrank test and Wilcoxon test, and the Cox proportion hazards model. A comparison between the covariate-adjusted and unadjusted methods is also illustrated with an oncology clinical trial example.

MeSH terms

  • Bias*
  • Clinical Trials as Topic / statistics & numerical data*
  • Data Interpretation, Statistical*
  • Humans
  • Medical Oncology
  • Models, Statistical
  • Proportional Hazards Models
  • Statistics, Nonparametric*
  • Treatment Outcome