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• Next, select the Time, Event, and Event status using the drop-down window. Partek Flow will automatically guess factors that might be appropriate for these options. Click Next to proceed with the task. 

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• The predictors (factors or variables) and co-predictors in the model must be defined. Co-predictors are numeric or categorical factors that will be included in the cox regression model. Time-to-event will be performed on features (e.g. genes) by default unless Use feature expression as predictor is unchecked. If unchecked, select a factor and Add factors that is not features to model a different variable. Using the default setting, Use feature expression as predictor, lets the user Add factors to the model that act to explain the relationship for time-to-event (co-predictor) in addition to features. Choose Add interaction to add co-predictors with known dependencies. If factors are added here, they cannot be added as stratification factors. Click Next to proceed with the task. 

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• The results of Cox regression analysis provide key information to interpret, including:
  • Hazard ratio (HR): if the HR = 0.5 then half as many patients are experiencing the event compared to the control group, if the HR = 1 the event rates are the same in both groups, and if the HR = 2 then twice as many are experiencing an event compared to the control group. 
  • HR limit: this is the confidence interval of the hazard ratio. 
  • P-value: the lower the p-value, the greater the significance of the observation. 

(e.g. If you have selected both a co-predictor and strata factor then a comparison using the co-predictors and Type III p-value  for the co-predictor will be generated in the Cox regression report.)

Kaplan-Meier Survival Curve 

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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 also feature specific (Figure 3). Select the step-plot icon under View to visualize the Kaplan-Meier survival curves for each gene. 

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

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

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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” (Figure 1) and the other attributes should be marked as stratification factors (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. 

Troubleshooting

If the task fails (no report is produced), please follow the directions in Reporting a problem.

If the task report is produced, but the results are missing for some features, it may be possible to fix the issue by following the directions in the Differential Analysis Troubleshooting section.

References

[1] Kaplan-Meier (product limit) estimator: https://en.wikipedia.org/wiki/Kaplan%E2%80%93Meier_estimator

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