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A survival outcome is recorded as two numbers per patient: how long they were followed, and whether the event (death, or progression) happened by then.
Survival analysis is the branch of statistics for the expected time until an event such as death occurs (Wikipedia). In datasets such as TCGA the outcome is stored as a time (days to death or to last follow-up) and an event flag (vital status), from which overall survival is computed; progression-free survival needs progression dates, which TCGA records unevenly. Follow-up in TCGA is short for many cancers, so event counts are low and survival models trained on it are noisy.
Shares Censoring and events in survival data, Kaplan-Meier curve, censoring and proportional hazards, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Censoring and events in survival data, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.