The Cox model is the preferred survival analysis technique. We compare parametric techniques with the Cox model. 709 consecutive @entity1 treated at a single institution. Univariate survival analysis was performed using the Cox model and parametric models. Significant factors were used to perform the multivariate analysis. The Cox model identified @entity1619 stage, N stage, @entity5 thickness, and lymphatic embolization as significant in multivariate analysis. Non-proportional hazards were demonstrated for post-operative radiotherapy and vascular invasion. In the exponential model, @entity1619 stage, N stage, post-operative radiotherapy, and @entity5 thickness were significant. The Weibull model identified @entity1619 stage, N stage, ASA score, post-operative radiotherapy, and vascular invasion as significant. Both lognormal and generalized gamma models identified @entity1619 stage, N stage, post-operative radiotherapy, @entity5 thickness, and vascular invasion as significant. Martingale and Cox-Snell residuals were tested. Internal validation confirmed the failure of the Cox model to correctly identify all significant covariates. In conclusion, parametric models may perform better than Cox model at identifying prognostic factors in certain circumstances and should be tested.

A critical appraisal of different survival techniques in @entity5 XXXX .

@entity1 :: ('9606', 'Species') :: ['patients'] @entity1619 :: ('MESH:D015458', 'Disease') :: ['T'] @entity5 :: ('MESH:D009369', 'Disease') :: ['tumor']
@entity1 :: (9606,Species) :: ['patients']
