Calibration of Cox model



I am using Cox model for risk prediction, and I am kind of lost regarding calibration. I am reading on Nam D’Agostino test, but I am unable to grasp the time period to be selected for calculating predicted probabilities. The predicted probabilities vary depending on the time period I select for calculating the probabilities, which directly affects my observed vs predicted probabilities/rates across deciles. I was thinking of using the mean follow-up time in the data to calculate predicted probabilities but a post on statsexchange recommended against using it. I am not sure how to go about this.

(I am using administrative claims data with significant right censoring. Because the enrollment information is month-to-month, my predicted probabilities are monthly)

Thank you.


It is not appropriate to use deciles or any other binning of continuous predicted values. My RMS book and course notes go into details related to obtaining smooth semi-parametric calibration curves for survival models, for a single time horizon. This is implemented in the R rms package calibrate.cph function. For examining the entire range of times and not selecting a single time point, there are residual-based methods you may want to take a look at, e.g., see the R rms package val.surv function.