Dear All, I have analysed time to event data for continuous variables by considering the multivariable fractional polynomial (MFP) model and comparing this to the untransformed and log transformed model to determine which transformation, if any, is best. This was possible as the Cox model was the underlying model. However, I am now at the situation where the assumption that the competing risks are independent is no longer true and therefore I cannot use the Cox model. The code I used to get the MFP model was: coxfitf <- mfp(Surv(with.Withtime,with.Wcens)~fp(all.age),family=cox,data=nearma,select=0.05,verbose=TRUE) where with.Withtime is the time to treatment withdrawal, with.Wcens is the censoring indictor for the event and all.firstint is the age at baseline. To look at the competing risks regression modelling when age in untransformed, I can use the following code: fitn<-crr(nearma$with.Withtime,censaeb,as.matrix(nearma$all.age),failcode=2,cencode=0) where censaeb is the censoring indicator which is coded 1 for the event of interest (time to treatment failure), 2 for the competing risk and 0 for the censored value. Can anyone suggest how I can effectively combine these situations i.e. is there a way to apply the fractional polynomail transformation to the variable to assertain whether the transformation improves the model fit? I've tried the following code but it doesn't actually transform the variable: fitf=crr(nearmb$with.Withtime,censaeb,as.matrix(fp(nearmb$all.firstint)),failcode=2,cencode=0) Thank you for your help, Laura