
Summary Many robust tests have been proposed in the literature to compare two hazard rate functions, however, very few of them can be used in cases when there are multiple hazard rate functions to be compared. In this article, we propose an approach for detecting the difference among multiple hazard rate functions. Through a simulation study and a real-data application, we show that the new method is robust and powerful in many situations, compared with some commonly used tests.
Reproducibility of Results, counting process, crossing, asymptotically independent, Sensitivity and Specificity, Survival Analysis, Applications of statistics to biology and medical sciences; meta analysis, survival data, Testing in survival analysis and censored data, Humans, Longitudinal Studies, Algorithms, Proportional Hazards Models
Reproducibility of Results, counting process, crossing, asymptotically independent, Sensitivity and Specificity, Survival Analysis, Applications of statistics to biology and medical sciences; meta analysis, survival data, Testing in survival analysis and censored data, Humans, Longitudinal Studies, Algorithms, Proportional Hazards Models
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