Kaplan-Meier Survival Analysis

		Statistics
	Back to Overview of Aabel Statistics

Aabel allows Kaplan-Meier analysis of both raw and summarized survival data. Aabel also provides a method for pivoting & summarizing raw survival data (if required).

A time variable that represents the time to the event (death, failure, recurrent failure, etc.)
A status variable that must contain both the event code and censor code, and can additionally contain a 3rd code representing the subjects at risk beyond completion of the study.
In order to split the data (e.g., subdividing subjects, conditions, treatments, etc.), the data also should contain a grouping variable. When a grouping variable is used to split the data, the auto-generated legend differentiating the survival curves will be based on categories of the grouping variable (see the example below).

Logrank Significance Test

Survival curves compare the cumulative probability of survival at any specific time. When we have generated two or more survival curves, the logrank test is used to determine whether the differences in survival between groups, treatments, etc., are more than we would expect by chance alone.

When comparing 3+ survival curves, the logrank tests for differences are pair-wise by default (i.e., the test for each pair is performed independent of other groups). You can also compare pairs while taking into account all groups.
The logrank test results include the hazard Ratio (i.e., the risk factor for one group, treatment, etc. compared to another group, treatment. etc.), the logrank chi-square and p values.
The survival curves and log rank test results shown below were generated in Aabel using the published data of Bland, M. (2000): gallstone-free survival after the dissolution of single and multiple gallstones.