I would like to use concordance to have an idea of my models discrimination power. Because I have time dependent covariates, the time to event data is in a "long" format, i.e. one row per time unit per individual. (dataset approx 1.5million rows)
I use the R package
survival, which provides a concordance measure in the model output, as well as a
I obtain unexpectedly high concordance estimates and very narrow confidence intervals (e.g. C=0.81 se=0.001)
1- I am wondering if the estimates are correct with data in the long format. At the very least I reckon the standard error is artificially lower because of the number of rows?
2- If I wanted to calculate concordance manually going back to the subject level, I have the time-to-event, but what risk estimate can I use? (as it is time dependent)
3- There is also a frailty term in my model. Are concordance calculations still valid in this case?
I've looked in the package documentation but can't find any mention of these issues.