jmcsBig: Joint model for BIG data using FastJM

In jmBIG: Joint Longitudinal and Survival Model for Big Data

Joint model for BIG data using FastJM

Description

function for joint model in BIG DATA using FastJM

Usage

jmcsBig(dtlong, dtsurv, longm, survm, samplesize = 50, rd, 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	sample size to divide the Big data
rd	random effect part
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

Li, Shanpeng, et al. "Efficient Algorithms and Implementation of a Semiparametric Joint Model for Longitudinal and Competing Risk Data: With Applications to Massive Biobank Data." Computational and Mathematical Methods in Medicine 2022 (2022).

See Also

jmbayesBig,jmstanBig,joinRMLBig

Examples

  
##
library(survival)
library(dplyr)
fit2<-jmcsBig(dtlong=data.frame(long2),dtsurv = data.frame(surv2),
longm=y~ x7+visit,survm=Surv(time,status)~x1+visit,rd= ~ visit|id,samplesize=200,id='id')
print(fit2)
##
  

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