Joint model for BIG data using FastJM

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.

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).

Author

Atanu Bhattacharjee, Bhrigu Kumar Rajbongshi and Gajendra Kumar Vishwakarma

Examples

  # \donttest{
##
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)
#> 
#>  Joint model for Big data using FastJM
#>  Call: 
#> jmcsBig(dtlong = data.frame(long2), dtsurv = data.frame(surv2), 
#>     longm = y ~ x7 + visit, survm = Surv(time, status) ~ x1 + 
#>         visit, samplesize = 200, rd = ~visit | id, id = "id")
#> 
#> 
#>  Total observation in longitudinal data: 1000 
#> 
#>  Chunk size: 200 
#> 
#>  Longitudinal process: 
#>             Estimate    SE Zvalue Pvalue
#> (Intercept)    9.130 0.483 18.896  0.000
#> x7            -0.028 0.008 -3.571  0.000
#> visit         -0.085 0.054 -1.592  0.111
#> sigma^2        0.596 0.012 48.186  0.000
#> 
#>  Survival process: 
#>          Estimate    SE ZValue Pvalue
#> x11_1     -0.032 0.257 -0.123  0.902
#> visit_1   -0.145 0.097 -1.497  0.134
#> 
#>  Association parameters :
#>               Estimate    SE Zvalue Pvalue
#> (Intercept)_1    0.219 0.144  1.522  0.128
#> visit_1          0.272 0.844  0.322  0.748
#> 
#>  Variance Covariance matrix of Random effects:
#>           Intercept  visit
#> Intercept     2.141 -0.376
#> visit        -0.376  0.141
##
  # }