Multiple testing procedures based on weighted Kaplan–Meier statistics for right-censored survival data

Abstract

In clinical trials or drug development studies, researchers are often interested in identifying which treatments or dosages are more effective than the standard one. Recently, several multiple testing procedures based on weighted logrank tests have been proposed to compare several treatments with a control in a one-way layout where survival data are subject to random right-censorship. However, weighted logrank tests are based on ranks, and these tests might not be sensitive to the magnitude of the difference in survival times against a specific alternative. Therefore, it is desirable to develop a more robust and powerful multiple testing procedure. This paper proposes multiple testing procedures based on two-sample weighted Kaplan-Meier statistics, each comparing an individual treatment with the control, to determine which treatments are more effective than the control. The comparative results from a simulation study are presented and the implementation of these methods to the prostate cancer clinical trial and the renal carcinoma tumour study are presented.