Comments (2)
ymhyumenghan
commented on July 21, 2024
'm sorry if I didn't describe my question well, but my question is not about the content of the data set. What I mean is that using composite data, your deephit_competing_risks presents a c_index result that is 0.3 points worse than the one the author wrote in the paper. Not sure how to solve this problem?
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Raymvp
commented on July 21, 2024
'm sorry if I didn't describe my question well, but my question is not about the content of the data set. What I mean is that using composite data, your deephit_competing_risks presents a c_index result that is 0.3 points worse than the one the author wrote in the paper. Not sure how to solve this problem?
Please note that in the example ipynb, "The survival function obtained with predict_surv_df is the probability of surviving any of the events, and does, therefore, not distinguish between the event types.
This means that we evaluate this "single-event case" as before."
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Related Issues (20)
- is_monotonic should be update to is_monotonic_increasing HOT 1
- Issue in function cox_time.py HOT 1
- L1 and L2 penalty coxph HOT 1
- AssertionError： assert durations.shape[0] == surv.shape[1] == surv_idx.shape[0] == events.shape[0]
- METABRIC Covariates Subset HOT 1
- AttributeError: 'Series' object has no attribute 'is_monotonic' HOT 17
- about hazard value! HOT 2
- Reproduction of the results in JMLR19 paper HOT 1
- Calculating Estimated Population Survival Curve HOT 4
- AttributeError: 'DeepHitSingle' object has no attribute 'state_dict' HOT 1
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- A model to add
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