Timeline for How to compare predictive power of survival models?
Current License: CC BY-SA 3.0
4 events
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| Nov 17, 2015 at 14:12 | comment | added | EdM | @StuartLacy I don't think that the underlying issues are much different between classification and survival analysis. It's just that the life-or-death types of choices implicit in survival analysis highlight the issue that different types of classification errors have different consequences. Whether the application is classification or estimating survival, using AUC as a metric implicitly assumes that all mis-classifications have the same cost. Similarly, if there are costs to acquiring predictors, those also should be considered in prospective classification schemes. | |
| Nov 17, 2015 at 11:03 | comment | added | Stuart Lacy | I appreciate the advice and can see the benefits of this approach. In classification one can build a group of models from different learning algorithms with their hyper-parameters optimised by cross-validation, using accuracy (or AUC) as a guiding metric. Why is it generally less straight forward in survival analysis? | |
| Nov 17, 2015 at 10:58 | vote | accept | Stuart Lacy | ||
| Nov 16, 2015 at 15:54 | history | answered | EdM | CC BY-SA 3.0 |