What does external validation of a Cox model mean?
Unlike for logistic regression models, external validation of Cox models is sparsely treated in the literature. Successful validation of a model means achieving satisfactory discrimination and calibration (prediction accuracy) in the validation sample.
What is the difference between internal and external validation?
Internal validation essentially means reusing parts or all of the dataset on which a model was developed to assess the likely overfit and correct for the resulting ‘optimism’ in the performance of the model. External validation means assessing the performance of a model already developed when applied to an independent dataset.
What are the products of a Cox model?
In the section ‘Products of a Cox model’, we describe key quantities relating to validation that may be derived by fitting a Cox model. We next identify three increasingly detailed levels of information from the derivation dataset that support different aspects of validation.
How does a well calibrated Cox prognostic model work?
Patients predicted to be at higher risk should exhibit higher event rates than those deemed at lower risk. Calibration reflects prediction accuracy. A well-calibrated risk score or prediction rule assigns the correct event probability at all levels of predicted risk.
How to calculate C-index from external validation?
I really appreciate your help in calculating a c-index from an external validation with an independent data set.
How to calculate dxy and C-index in RMS?
I can successfully use validate () in the rms package to calculate the Dxy and c-index for my boot-strapped internal validation. Now I need a c-index for my external validation.
Are there any published results from a Cox model?
Published results from Cox models are usually restricted to the regression coefficients (log hazard ratios) and their standard errors or confidence intervals. Nevertheless, there is still much that can be done in validating Cox models.
What are the values of the row dxy?
The values corresponding to the row Dxy are equal to 2 * (C – 0.5) where C is the C-index or concordance probability. If the user is correlating the linear predictor (predicted log hazard) with survival time, Dxy is automatically negated.