Interpreting results: Comparing >2 survival curves

Logrank test

The P value tests the null hypothesis that the survival curves are identical in the overall populations. In other words, the null hypothesis is that the treatment did not change survival.

The P value answers this question:

If the null hypothesis is true, what is the probability of randomly selecting subjects whose survival curves are as different (or more so) than was actually observed?

Logrank test for trend

If you entered three or more data sets, Prism automatically calculates the logrank test for trend. This test is only meaningful if the data sets were entered in a logical order, perhaps corresponding to dose or age. If the data sets are not ordered (or not equally spaced), then you should ignore the results of the logrank test for trend.

The logrank test for trend calculates a P value testing the null hypothesis that there is no linear trend between column order and median survival. If the P value is low, you can conclude that there is a significant trend.

Multiple comparison tests

After comparing three or more treatment groups, you may want to go back and compare two at a time. Prism does not do this automatically, but it is easy to duplicate the analysis, and change the copy to only compare two groups. But if you do this, you need to adjust the definition of 'significance' to account for multiple comparisons.