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crtprep
2018 Feb 16
0
Competing risks - calibration curve
...,2.54), dist.cens = "lnorm", anc.cens = 3.5, beta0.cens = 5.42, z=NULL, beta = list(c(0.21, 0.017), c(0.37, 0.016)), x=list(c("normal",0,1), c("bern",0.564)), nsit=2)
table(status)
table(cause)
df$cause<-ifelse(is.na(df$cause),0,df$cause)
table(df$cause)
df.w<-crprep("time","cause", data=df, trans=c(1,2), cens=0, id="nid", keep=c("x", "x.1"))
with(df.w,table(failcode,status))
ddist<-datadist(df.w)
options(datadist='ddist')
mod<-cph(Surv(Tstart,Tstop,status==1)~rcs(x,3)+x.1,data=df.w, weight=weight...
2018 Mar 21
1
selectFGR vs weighted coxph for internal validation and calibration curve- competing risks model
Dear Geskus,
I want to develop a prediction model. I followed your paper and analysed thro' weighted coxph approach. I can develop nomogram based on the final model also. But I do not know how to do internal validation of the model and subsequently obtain calibration plot. Is it possible to use Wolbers et al Epid 2009 approach 9 (R code for internal validation and calibration) . It is