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Cox regression (Cox proportional-hazards model) tests the effects of factors (predictors) on survival time. Predictors that lower the probability of survival at a given time are called risk factors; predictors that increase the probability of survival 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 should not be used for a small sample size (n < 40) because the events could accidently concentrate into one of the cohorts leading to an infinite hazard ratio which will not produce meaningful results (Xu R, Shaw PA, Mehrotra DV. Hazard ratio estimation in small samples. Stat Biopharm Res. https://shawstat.org/wp-content/uploads/2021/03/Hazard-ratio-estimation-in-small-samples.pdf)

Configuring the Cox Regression Dialogue 

• Open the Cox Regression task in the task menu under Statistics. 

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