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Understanding the Cox Regression Models with

By Time-change Covariates and Mai Zhou


The Cox regression model is a cornerstone of modern survival analysis and is widely used in many other fields as well. But the Cox models with time-change covariates are not easy to understand or visualize. We therefore offer a simple and easy-to-understand interpretation of the (arbitrary) baseline hazard and time-change covariate. This interpretation also provides a way to simulate variables that follow a Cox model with arbitrary baseline hazard and time-change covariate. Splus/R codes to generate/fit various Cox models are included. Frailty model is also included. KEY WORDS: Arbitrary baseline hazard; Splus code; Simulations. The Cox regression model is invariably difficult for students to grasp, partly because it is so different from the classical linear regression models. The added concept of time-change covariates further increases the difficulty. After several years of teaching a master’s level survival analysis course, we have settled on a teaching approach that uses exponential distributions in conjunction with a transformation to the Cox model. This approach is not found in the current tex

Year: 2009
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