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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. 

In this tutorial, we will use Cox Regression can be used to assess whether gene expression is associated with survival. to test the effects of tumor gene expression on survival time while accounting for tumor size.   

Performing Cox Regression Analysis

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Advanced options such as the inclusion of interactions between predictors and co-predictors can be accessed by selecting Model... (Figure 3) and the Results... button invokes a dialog (Figure 4) with additional output options for the results spreadsheet. We do not need to adjust any of the advanced model or output options for this tutorial.  

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11. modelfit(0) - P-value of the test assessing the overall model fit, i.e., the relationship between survival time, the predictor, and co-predictors in the model. A modelfit value of > 0.05 indicates a low association between the predictor and/or co-predictors with survivial survival time. 

Please note that the Cox Regression results spreadsheet is a temporary file. If you would like to be able to view the spreadsheet again after closing Partek Genomics Suite, be sure to save it by selecting the Save Active Spreadsheet icon ().

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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 occurring at any given point in time, controlling for other predictors in the model.

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