Cox Regression Algorithm
A Cox Regression Algorithm is a linear model regression algorithm that fits Cox's proportional hazards model.
- AKA: Proportional Hazards Regression.
- See: Ridge Regression, Lasso Regression, Hazards Ratio, Survival Analysis.
References
2012
- http://www.statsdirect.com/help/default.htm#survival_analysis/cox_regression.htm
- QUOTE: This function fits Cox's proportional hazards model for survival-time (time-to-event) outcomes on one or more predictors.
Cox regression (or proportional hazards regression) is method for investigating the effect of several variables upon the time a specified event takes to happen. In the context of an outcome such as death this is known as Cox regression for survival analysis. The method does not assume any particular "survival model" but it is not truly nonparametric because it does assume that the effects of the predictor variables upon survival are constant over time and are additive in one scale. You should not use Cox regression without the guidance of a Statistician.
Provided that the assumptions of Cox regression are met, this function will provide better estimates of survival probabilities and cumulative hazard than those provided by the Kaplan-Meier function.
- QUOTE: This function fits Cox's proportional hazards model for survival-time (time-to-event) outcomes on one or more predictors.