Please enable JavaScript to view this site.

Navigation: STATISTICS WITH PRISM 9 > Survival analysis

How to: Cox proportional hazards regression

Scroll Prev Top Next More

Cox proportional hazards regression is used to estimate the effect of various predictor variables on the elapsed time to some event of interest. Often (especially in the biological sciences), this event of interest is death, giving survival analysis its name. The goal of Cox proportional hazards regression is to generate a model for the hazard rate of the observed population, which is directly related to the survival function of this population. This can then be used to generate survival curves for specific groups or individuals within the population (based on values of the predictor variables in the model). Use the links below to learn more about how to perform Cox proportional hazards regression within Prism.

 

A word of caution!

Cox proportional hazards regression was introduced in Prism 9.3.0 as the newest (and arguably most advanced) Prism Labs feature. This analysis is very-well established as the industry standard for survival analysis, and allows for complex investigations of multiple different kinds of predictor variables (both categorical and continuous) and their effect on survival. We've gone to great lengths to ensure that the results Prism generates are accurate, and within these guide pages, you'll find numerous explanations for how these results are generated, as well as basic guidance for how to interpret many of these results.

HOWEVER, Cox regression is advanced - arguably more advanced than any other analysis available within Prism. Before analyzing your data with Cox regression, be sure that you understand the fundamentals of survival analysis (i.e. Kaplan-Meier survival estimation and the various tests available for comparing the resulting survival curves: the logrank test, the logrank test for trend, and the Gehan-Breslow-Wilcoxon test). Cox regression also relies heavily on statistical concepts that power other forms of multiple regression (like multiple linear and multiple logistic regression). Even with knowledge of all of these different concepts, the best advice is always to seek guidance or assistance from a statistician when dealing with these complex techniques.

 

Entering data for Cox proportional hazards regression

Performing Cox proportional hazards regression

 Defining a model for Cox proportional hazards regression

 Setting reference levels for Cox regression

 Predicting values with Cox proportional hazards regression

 Comparing Cox proportional hazards regression models

 Options for Cox proportional hazards regression

 Goodness-of-fit options for Cox proportional hazards regression

 Residuals for Cox proportional hazards regression

 Graphs of estimated survival curves

© 1995-2019 GraphPad Software, LLC. All rights reserved.