I'm doing a survival analysis for cancer patients. I have two different models for two different types of cancer (different datasets).
Some key differences between model 1 and 2 are:
- Model 1 has only 152 covariates and model 2 has 650
- Model 1 subjects are being clustered and model 2 not
Model 1 seems to be well fit when analyzing p-value for the covariates. A total of 29 covariates have p-values<0.005
My problem is Model 2 shows a c-index of 0.83, however not even one covariate has p-value<0.005. Why is this happening and shouldn't model 2 perform way worse?
<lifelines.CoxPHFitter: fitted with 519 total observations, 333 right-censored observations> duration col = 'duration_col' event col = 'vital_status.Dead' cluster col = 'paper_iCluster.Group' penalizer = 0.1 l1 ratio = 0.0 robust variance = True baseline estimation = breslow number of observations = 519 number of events observed = 186 partial log-likelihood = -919.93 Concordance = 0.79 Partial AIC = 2143.85 log-likelihood ratio test = 161.27 on 152 df -log2(p) of ll-ratio test = 1.80
<lifelines.CoxPHFitter: fitted with 495 total observations, 283 right-censored observations> duration col = 'duration_col' event col = 'vital_status.Dead' penalizer = 0.1 l1 ratio = 0.0 baseline estimation = breslow number of observations = 495 number of events observed = 212 partial log-likelihood = -974.79 Concordance = 0.83 Partial AIC = 3249.57 log-likelihood ratio test = 307.35 on 650 df -log2(p) of ll-ratio test = -0.00