Cox regression is a semi-parametric statistical method within survival analysis designed to model the relationship between multiple predictor variables and time-to-event outcomes while accounting for censored data. The core principle relies on the proportional hazards assumption, positing that the hazard ratio associated with each covariate remains constant over time relative to other factors in the dataset. This framework allows researchers to isolate the specific influence of independent variables—such as drug treatments or biological markers—on survival curves without specifying an underlying baseline distribution for survival times.
Prerequisite chain context: requires Event Rate Estimation at Risk Sets.