And - in Prism - creating graphs of these estimated survival curves for each of these different groups is easy. Using all of the information collected for all of the individuals (in all of the different possible groups), Cox regression is able to generate survival estimates for any individual or group with any combination of predictor variable values. a 'Treatment' group and a 'Control' group) can be statistically compared, there's no way to include continuous predictor variables (such as age, blood pressure, weight, etc.) into these estimations.Ĭox proportional hazards regression (or just Cox regression) is an advanced statistical method of survival analysis that allows for the incorporation of any number of predictor variables (both categorical and continuous predictor variables) into the model. Although survival curves generated using the Kaplan-Meier method for different groups (i.e.

However, this technique is limited to creating survival curves for populations that are assumed to be homogeneous, and this method does not incorporate values for predictor variables that may have been collected for the individuals in the study (such as age, race, treatment group, etc.). For a long time, Prism has offered a very simple form of survival analysis that uses the Kaplan-Meier estimation of survival.

In the biological sciences, the 'event' being studied is often death (of animals in a given experimental group, of humans with a particular disease process, etc.), giving this analysis its name. Survival analysis is an extremely powerful statistical tool that is used to analyze 'time to event' data, and generate estimates for how the probability of this event occurring changes over time.

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