...

To see whether the survival curves are statistically different, Kaplan-Meier task runs Log-rank and Wilcoxon (aka Wilcoxon-Gehan) tests. The null hypothesis is that the survival curves do not differ among the groups (the computational details are available in [2]). When feature expression is used, the p-values are also feature specific also (Figure 3).

...

Like in Cox Regression task, it is possible to choose stratification factor(s) in GUI, but the purpose and meaning of stratification are not the same as in Cox Regression. Suppose we want to compare the survival among the six groups defined by the three levels FactorA and the two bins of feature expression. We can select the two factors on  “Select group factor(s)” page (Fig Figure 1). In that case, the reported p-values will reflect the statistical difference among the six survival curves that are due to both FactorA and the feature expression. Imagine that our primary interest is the effect of feature expression on survival. Although FactorA can be important and therefore should be included in the model, we want to know whether the effect of feature expression is significant after the contribution of FactorA is taken into account. In other words, the goal is to treat FactorA as a nuisance factor and the binned feature expression as a factor of interest.

Grouping of survival curves by the level of a specified factor. In qualitative terms, it is possible to obtain an answer if we group the survival curves by the level of FactorA. In Data Viewer, that can be achieved via “Grouping > Split by” function (Figure 4). That makes it easy to compare the survival curves that have the same level of FactorA and avoid the comparison of curves across different levels of FactorA.

...