Implementing Cox (proportional gazards) regression in Python
Now we will use another package to perform a specific method called Cox proportional hazards regression. First, let’s discuss the method itself.
Cox proportional hazard regression is a statistical method used in survival analysis to evaluate the relationship between the time until an event of interest occurs and predictor variables. This method was developed by Sir David Cox in 1972.
What is very good about this type of regression is that it can handle censoring of the data and allow us to create multivariate models. This is very useful when we want to add other covariates in the survival analysis, or adjust for other covariates such as age or type of tumors in oncology.
The Cox model is based on the hazard function, which describes the instantaneous rate of failure at a given time. The hazard is assumed to be proportional across different levels of the predictor variables. One of the key assumptions of the...
