An empirical comparison of statistical tests for assessing the proportional hazards assumption of Cox's model.
In the analysis of survival data using the Cox proportional hazard (PH) model, it is important to verify that the explanatory variables analysed satisfy the proportional hazard assumption of the model. This paper presents results of a simulation study that compares five test statistics to check the proportional hazard assumption of Cox's model. The test statistics were evaluated under proportional hazards and the following types of departures from the proportional hazard assumption: increasing relative hazards; decreasing relative hazards; crossing hazards; diverging hazards, and non-monotonic hazards. The test statistics compared include those based on partitioning of failure time and those that do not require partitioning of failure time. The simulation results demonstrate that the time-dependent covariate test, the weighted residuals score test and the linear correlation test have equally good power for detection of non-proportionality in the varieties of non-proportional hazards studied. Using illustrative data from the literature, these test statistics performed similarly.
Ng'andu
N H
NH
Department of Biostatistics, University of North Carolina, Chapel Hill 27599-7400, USA.
D015982:Bias; D003627:Data Interpretation, Statistical; D006801:Humans; D016014:Linear Models; D016016:Proportional Hazards Models; D016032:Randomized Controlled Trials as Topic; D015203:Reproducibility of Results; D016019:Survival Analysis; D013997:Time Factors