Joint model for BIG data using JMbayes2

function for joint model in BIG DATA using JMbayes2

Usage

jmbayesBig(
  dtlong,
  dtsurv,
  longm,
  survm,
  samplesize = 50,
  rd,
  timeVar,
  nchain = 1,
  id,
  niter = 2000,
  nburnin = 1000
)

Arguments

dtlong

longitudinal dataset, which contains id,visit time,longitudinal measurements along with various covariates

dtsurv

survival dataset corresponding to the longitudinal dataset, with survival status and survival time

longm

fixed effect model for longitudinal response

survm

survival model

samplesize

sample size to divide the Big data

rd

random effect model part

timeVar

time variable in longitudinal model, included in the longitudinal data

nchain

number of chain for MCMC

id

name of id column in longitudinal dataset

niter

number of iteration for MCMC chain

nburnin

number of burnin sample for MCMC chain

Value

returns a list containing various output which are useful for prediction.

References

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).

See also

jmcsBig,jmstanBig,joinRMLBig

Author

Atanu Bhattacharjee, Bhrigu Kumar Rajbongshi and Gajendra Kumar Vishwakarma

Examples

 # \donttest{
##
library(survival)
library(nlme)
#> 
#> Attaching package: 'nlme'
#> The following object is masked from 'package:dplyr':
#> 
#>     collapse
library(dplyr)
fit5<-jmbayesBig(dtlong=long2,dtsurv = surv2,longm=y~ x7+visit,survm=Surv(time,status)~x1+visit,
rd= ~ visit|id,timeVar='visit',nchain=1,samplesize=200,id='id')
ydt<-long2%>%filter(id%in%c(900))
cdt<-surv2[,'id']%>%filter(id%in%c(900))
newdata<-full_join(ydt,cdt,by='id')
P2<-predJMbayes(model<-fit5,ids<-c(900),newdata=newdata,process = 'event')
plot(P2$p1[[1]])

##
# }