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For each group, the survival curve (aka survival function) is estimated using Kaplan-Meier estimator [1]. For instance, if one selects FactorA with three levels and two feature expression bins, six survival curves are displayed in Data Viewer , as shown below(Figure 2).

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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 feature specific also , as seen below(Figure 3).

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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 (Fig 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.

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If in Fig Figure 4 we see one or more subplot where the survival curves differ a lot, that is evidence that the feature expression affects the survival even after adjusting for the contribution of FactorA. To obtain an answer in terms of adjusted Log-rank and Wilcoxon p-values, one should deselect FactorA as a “group factor” (Fig Figure 1) and mark it as a stratification factor instead (Fig Figure 5). The computational of stratification adjusted p-values is elaborated in [2].

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Suppose when the feature expression and FactorA are selected as “group factors” (Fig Figure 1), Log-rank p-value is 0.001, and when FactorA is marked as stratification factor, the p-value becomes 0.70. It means that FactorA is very useful for explaining the difference in survival while the feature factor is of no use if FactorA is already in the model. In other words, the marginal contribution of the binned expression factor is low. 

If more than two attributes are present, it is possible to measure the marginal contribution of any single factor in a similar manner: the attribute of interest should be selected as “group factor” (Fig Figure 1) and the other attributes should be marked as stratification factors (Fig Figure 5). There is no limit on the count of factors that can be selected as “group” or stratification, except that all of the selected factors are involved in defining the groups and the groups should contain enough samples (at least, be non-empty) for the results to be reliable. 

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