I have encountered a problem that I would like to ask for help: My purpose is to use competing risk (cr) model to train a survival data that contains multiple records within one patient. I tried to use a bunch of R packages and functions that are able to train mixed-effect cr model with a cluster(ID) (e.g. patient ID) added.
so far the R functions I tried and the results are: (1). FGR() worked fine for fixed-effect only cr model but cannot train a mixed-effect cr model; (2). CSC() is able to train a mixed-effect model but is unable to predict the risk probability using predict(); (3) comp.risk() worked fine for training and predicting a mixed-effect cr model, but it doesn't allow c-index calculation using c-index() or concordance.index().
Now the question is, how can I calculate the c-index for my validation set from the result of comp.risk() and predict(). I've gotten a fitted model and predicted risks in certain times (for instance, 3, 5, 10 year). Do I need to have predicted risks in all possible times in order to calculate the c-index? Is there a better way or simple solution for this?
Thank you very much for you all help.
If you have a survival object and a predictor, you can use the Hmisc library function to get a c-statistic:
library(Hmisc) #generate observed survival set.seed(42) censor
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