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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 This can be achieved via “Grouping in the Data Viewer by choosing Grouping > Split by” function by under Configure (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 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” (Figure 1) and mark it as a stratification factor instead (Figure 5). The computational computation 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” (Figure 1), Log-rank p-value is 0.001, and when FactorA is marked as stratification factor, the p-value becomes 0.70. It This 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. 

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