Nothing
#this function is interesting to get the baseline cumulative incidence function ONLY if you have alredy the coefficient estimate
#if not, use the cumulative incidence using the survfit function with a coxph object using the Geskus modified dataset
BBCI<-function( formula , time , status , trans = 1 , cens.code = 0 , data , beta = 0){
#get the model matrix
mm<-model.matrix(formula,data)
#get times
times<-data[,time]
#modify at risk set
times2<-times
times2[!data[,status]%in%c(cens.code,trans)]<-Inf
#Estimate the survival function of the censoring distribution g_hat
g1<-with(data,Surv(time=times,event=status==cens.code))
sg1<-survfit( g1~1 )
#to avoid errors
sg1$time[1]<-min(times)
#compute exp (sum of B'Z )
beta2<-c(0,beta)
bz<-t(exp(mm%*%beta2))
#get tj (times at which en event of interest occurs)
nt<-sort(times[data[,status]==trans])
#get the values of g_hat at tj and at all times
ghat<-sapply( nt , function(x) sg1$surv[tail(which( sg1$time <= x),1)])
ghat2<-sapply( times , function(x) sg1$surv[tail(which( sg1$time <= x),1)])
#get the results in matrix and compute the wheits for the breslow analog for 1-cuminc function
matg1.1<-matg<-matrix(ghat,nrow=length(times),ncol=length(nt),byrow=T)
matg2<-matrix(ghat2,nrow=length(times),ncol=length(nt))
matg3<- matg2 > matg
matg1.1[matg3]<-matg2[matg3]
matg4<-matg/matg1.1
#get the matrix of indicators of times in the modified risk set
matt4<-matrix(times,nrow=length(times),ncol=length(nt))
matt<-matrix(times2,nrow=length(times),ncol=length(nt))
matt2<-matrix(nt,nrow=length(times),ncol=length(nt),byrow=T)
matt3<-matt >= matt2
#combine indicators en wheits
matt5<-matt3*matg4
#get the Breslow-like baseline cumulative sub-distribution hazard
cs<-cumsum(1/(bz%*%matt5))
#get the Breslow-like baseline cumulative incidence function
s2<- 1 - exp(-cs)
#get the result in a data frame
df1<-data.frame(time = nt, est = s2)
return(df1)
}
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