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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 because the events could accidently concentrate into one of the cohorts leading to an infinite hazard ratio which will not produce meaningful results. Click here to read more about hazard ratio estimation in small samples.

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• The user can select categorical factors to perform stratification if needed. Stratification is used when proportional hazard assumptions are violated or not constant over time with co-predictors. Stratified Cox regression accounts for non-proportional hazards over time by optimizing hazard strata then fitting the stratified Cox regression model. In other words, the data is split into subgroups based on the categorical variable and the model is re-estimated. This accounts for the effect of a co-predictor that varies over time. needed because the proportional odds assumption holds only within each stratum, but not across the strata. Click Finish to complete the task. 

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