To perform calibration of a Cox regression model (i.e. assessing for the agreement between the predicted and the observed outcome), what is the best method to present the accuracy of the model in predicting the actual event?
As far as I understand, we can calculate the actual outcome probability by observing the number of events that occurred in a number of subjects with similar/same predicted probability from the Cox model. To perform the above calculation, do we stratify the predicted risk into several groups (<15%, 15-30%, 30-45% etc.), and within each risk group we use the number of subjects as the denominator for the calculation of actual outcome?
What method do we use to compare the predicted outcome with the actual outcome? Is it good enough if we simply present the predicted and actual risk% in each risk group in table format? Can rms package in R do all calibrations for you?
Can we use pec::predictSurvProb() to give the absolute risk of event for each individual? Can we specify the time point for the risk/hazard function for each individual to be at the ENDPOINT of follow up?
When interpreting the results, do we use the mean follow up period (in years) as the time point on which the predicted risk and actual risk are based? (E.g. Individual A has 30% risk of event at 6.5 years (mean follow up period))
Is the goodness-of-fit test for Cox regression (Gronnesby and Borgan test) simply a means for calibration for cox regression? Or does it mean something else?
To compare models with net reclassification, how many subjects and outcomes do we need for such method to become valid?