joinRMLBig: Joint model for BIG data using joineRML

In jmBIG: Joint Longitudinal and Survival Model for Big Data

Joint model for BIG data using joineRML

Description

function for joint model in BIG DATA using joineRML

Usage

joinRMLBig(dtlong, dtsurv, longm, survm, samplesize = 50, rd, timeVar, id)

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	model for longitudinal response
survm	survival model
samplesize	random effect part
rd	random effect part
timeVar	time variable in longitudinal model, included in the longitudinal data
id	name of id column in longitudinal dataset

Value

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

Author(s)

Atanu Bhattacharjee, Bhrigu Kumar Rajbongshi and Gajendra Kumar Vishwakarma

References

Hickey, Graeme L., et al. "joineRML: a joint model and software package for time-to-event and multivariate longitudinal outcomes." BMC medical research methodology 18 (2018): 1-14.

See Also

jmbayesBig,jmstanBig,jmcsBig

Examples

  
##
library(survival)
library(dplyr)
fit4<-joinRMLBig(dtlong=long2,dtsurv = surv2,longm=y~ x7+visit,survm=Surv(time,status)~x1+visit,
rd=~ visit|id,timeVar='visit',samplesize=200,id='id')
P2<-predJRML(model<-fit4,ids<-c(10),dtlong=long2,dtsurv=surv2)
pp1<-plot(P2$plong[[1]])
pp1<-plot(P2$psurv[[1]])
##
   

jmBIG documentation built on May 29, 2024, 6:04 a.m.