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Clinical covariates are the ordinary facts about a patient, such as age, stage, node count and whether they had chemotherapy or radiotherapy, that any prognostic model must beat or build on.
Survival models relate time to event to one or more covariates (Wikipedia on proportional hazards models). In cancer the routine ones are age, sex, stage, grade, nodal status, receptor status and treatment indicators for surgery, radiotherapy, chemotherapy and endocrine therapy. A molecular or imaging model is only useful if it adds discrimination over a clinical baseline built from these, and treatment flags are confounders as well as predictors because sicker patients are treated differently.
Shares Tissue-of-origin signal in tumour data, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Tissue-of-origin signal in tumour data, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares Tissue-of-origin signal in tumour data, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares External validation, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares External validation, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares External validation, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares External validation, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.
Shares External validation, Cancer AI vocabulary (CanSim terms map) and the tag cansim-terms.