Supplement 1. A sample of the R code is shown below:

install.packages("ltm")
install.packages("irtoys")
install.packages("catR")

require(ltm)
require(irtoys)
require(catR)
parallel<-matrix(c(1),nrow=6,ncol=2)

b_par<-read.table("E:\\med_cat_sim\\cat_simulations\\item_parameter_2.csv",header=T,sep=",")
for(k in 1:6){ 
 b<-b_par[,2]  
    d<-rep(1,1012)
    a1<-b_par[,1]
    b1<-b
    c1<-rep(0,1012)
EMT<-cbind(a1,b1,c1,d)
theta.gen1<-rnorm(83,0,1) 
####################  D=1, theta=0, c=0  ################ #######
Item_Para<-data.frame(EMT)
 colnames(Item_Para)<-list("a","b","c","d")

########## CAT Constraint ############
Params<-Item_Para
start1=list(seed=NA,nrItems=5,theta=1,startSelect="MFI")
test1<-list(method="ML",itemSelect="MFI")
test2<-list(method = "ML", priorDist = "Jeffreys", randomesque = 4)
final<-list(method="EAP")
stop1<-list(rule="precision",thr=0.05*k)
#stop1<-list(rule="length",thr=n)
res1 <- simulateRespondents(theta.gen1, Params, start = start1,test = test2, stop = stop1,
final = final)
  
estimate<-cbind(res1$final.value$true.theta, res1$final.value$estimated.theta,
res1$final.value$final.SE,res1$final.value$total.items.administrated)
   
parallel[k,1]<-cor(estimate[,1],estimate[,2])   # 1
parallel[k,2]<-mean(estimate[,4])
}    
parallel
rownames(parallel)<-c("SEM=0.05","SEM=0.1","SEM=0.15","SEM=0.2","SEM=0.25","SEM=0.3")