A New Method for the Comparison of Survival Distributions

• Activity Number: 360
• Type: Contributed
• Date/Time: Tuesday, August 3, 2010 : 10:30 AM to 12:20 PM
• Sponsor: Biometrics Section
• Author(s): Xun Lin*+ and Qiang Xu
• Companies: Pfizer Global Research and Development and FDA
• Keywords: Survival analysis ; log-rank test ; Wilcoxon test ; Kolmogorov-Smirnov test ; statistical power ; Type I error

Abstract

The assessment of overall homogeneity of time-to-event curves is a key element in survival analysis in biomedical research. The currently commonly used testing methods, e.g., log-rank test, Wilcoxon tests, and Kolmogorov-Smirnov test, may have a significant loss of statistical testing power under certain circumstances. We propose a new testing method that is robust for the comparison of the overall homogeneity of survival curves based on the absolute difference of the area under the survival curves using normal approximation by Greenwood's formula. Monte Carlo simulations are conducted to investigate the performance of the new testing method compared against the log-rank, Wilcoxon, and Kolmogorov-Smirnov tests under a variety of circumstances. Furthermore, the applicability of the new testing approach is illustrated in a real data example from a kidney dialysis trial.