jmbB | R Documentation |
JMbayes2
The function fits joint model for survival data with two events. It utilizes the JMbayes2 package for obtaining the model parameter estimates.
jmbB(
dtlong,
dtsurv,
longm,
survm,
rd,
timeVar,
id,
samplesize = NULL,
BIGdata = FALSE,
niter = 200,
nburnin = 100,
nchain = 1
)
dtlong |
longitudinal data |
dtsurv |
survival data with two event status along with event time |
longm |
longitudinal model e.g. list(serBilir~drug * year,serBilir ~ drug * year) |
survm |
survival model e.g. list(Surv(years,status2)~drug,Surv(time_2,status_2)~drug+age) |
rd |
random effect component e.g. list(~year|id,~year|id) |
timeVar |
time variable |
id |
ID variable |
samplesize |
samplesize for bigdata |
BIGdata |
logical argument TRUE or FALSE |
niter |
number if iteration |
nburnin |
number of sample to burn |
nchain |
number of MCMC chain |
Estimated model parameters of Joint model with bidirectional survival data
Atanu Bhattacharjee, Bhrigu Kumar Rajbongshi and Gajendra Kumar Vishwakarma
Rizopoulos, D., G. Papageorgiou, and P. Miranda Afonso. "JMbayes2: extended joint models for longitudinal and time-to-event data." R package version 0.2-4 (2022).
Bhattacharjee, A., Rajbongshi, B. K., & Vishwakarma, G. K. (2024). jmBIG: enhancing dynamic risk prediction and personalized medicine through joint modeling of longitudinal and survival data in big routinely collected data. BMC Medical Research Methodology, 24(1), 172.
library(JMbayes2)
st_pbcid<-function(){
new_pbcid<-pbc2.id
new_pbcid$time_2<-rexp(n=nrow(pbc2.id),1/10)
cen_time<-runif(nrow(pbc2.id),min(new_pbcid$time_2),max(new_pbcid$time_2))
status_2<-ifelse(new_pbcid$time_2<cen_time,1,0)
new_pbcid$status_2<-status_2
new_pbcid$time_2<-ifelse(new_pbcid$time_2<cen_time,new_pbcid$time_2,cen_time)
new_pbcid$time_2<-ifelse(new_pbcid$time_2<new_pbcid$years,new_pbcid$years,new_pbcid$time_2)
new_pbcid
}
new_pbc2id<-st_pbcid()
pbc2$status_2<-rep(new_pbc2id$status_2,times=data.frame(table(pbc2$id))$Freq)
pbc2$time_2<-rep(new_pbc2id$time_2,times=data.frame(table(pbc2$id))$Freq)
pbc2_new<-pbc2[pbc2$id%in%c(1:100),]
new_pbc2id<-new_pbc2id[new_pbc2id$id%in%c(1:100),]
model_jmbBdirect<-jmbB(dtlong=pbc2_new,dtsurv =new_pbc2id,
longm=list(serBilir~drug*year,serBilir~drug*year),
survm=list(Surv(years,status2)~drug,Surv(time_2,status_2)~drug+age),
rd=list(~year|id,~year|id),
id='id',timeVar ='year')
model_jmbBdirect
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