KM tests can be satisfactory in many situations, especially during preliminary analysis; however, KM tests are non-parametric, and typically are less powerful than parametric equivalents. Cox regression extends survival analysis to a parametric regression type framework under which it assumes more power. If there are several independent variables that need to be incorporated into a model, and some of them are continuous, it is advantageous to perform cox proportional hazard modeling rather than KM.
Cox modeling also starts with creating a survival object, as we did in previous examples. Other than that, a cox model looks very similar to a standard regression model with the response variables specified to the left of the ~ and the independent variables specified to the right.
In cox regression modeling, we use the coxph() function over the surv() function to specify the dependent variable. This can be done directly in the formula, or by assigning it to...
