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Cox regression (Cox proportional-hazards model) enables assessing tests the effect effects of several factors (predictors) on the outcomesurvival time. Predictors that lower the probability of survival (not experiencing the event) at a given time are called risk factors; predictors that increase the probability of survival (not experiencing the event) at a given time are called protective factors. The Cox proportional-hazards model are similar to a multiple logistic regression that considers time-to-event rather than simply whether an event occurred or not. Cox Regression can be used to assess whether gene expression is associated with survival.  

In this tutorial, we will use Cox Regress to test the effects of tumor gene expression on survival time while accounting for tumor size.   

Performing Cox Regression Analysis

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The hazard ratio , also known as relative risk, is an effect size measure used to assess the direction and magnitude of the effect of a predictor variable on the relative risk likelihood of the event occuring at any given point in time, controlling for other predictors in the model.

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