Prediction using FastJM — survfitJMCS • jmBIG

prediction of survival probability using FastJM for BIG data

Usage

survfitJMCS(model, ids, u, method = "GH", obs.time)

Arguments

model: fitted model object
ids: value of id
u: see survfitjmcs
method: options are 'Laplace','GH'
obs.time: vector which represents time variable in the longitudinal data

Value

list of predicted value for the given id along with other information relevant for survival probability confidence plot

Examples

  # \donttest{
##
library(survival)
library(dplyr)
jmcs1<-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')
mod2<-jmcs1
P2<-survfitJMCS(model<-mod2,ids<-c(5),u<-seq(surv2[surv2$id==5,]$time,
surv2[surv2$id==5,]$time+10,0.2),obs.time='time')
print(P2)
#> 
#> Prediction of Conditional Probabilities of Event
#> based on the pseudo-adaptive Guass-Hermite quadrature rule with 6 quadrature points
#> $`5`
#>        times  PredSurv
#> 1   2.351878 1.0000000
#> 2   2.351878 1.0000000
#> 3   2.551878 0.9893133
#> 4   2.751878 0.9573583
#> 5   2.951878 0.9573583
#> 6   3.151878 0.9573583
#> 7   3.351878 0.9573583
#> 8   3.551878 0.9573583
#> 9   3.751878 0.9573583
#> 10  3.951878 0.9464924
#> 11  4.151878 0.9356432
#> 12  4.351878 0.9356432
#> 13  4.551878 0.9139051
#> 14  4.751878 0.8809480
#> 15  4.951878 0.8809480
#> 16  5.151878 0.8589332
#> 17  5.351878 0.8368443
#> 18  5.551878 0.8145375
#> 19  5.751878 0.7922527
#> 20  5.951878 0.7922527
#> 21  6.151878 0.7809792
#> 22  6.351878 0.7584506
#> 23  6.551878 0.7361916
#> 24  6.751878 0.7250806
#> 25  6.951878 0.7137624
#> 26  7.151878 0.7023804
#> 27  7.351878 0.6910631
#> 28  7.551878 0.6573517
#> 29  7.751878 0.6573517
#> 30  7.951878 0.6461274
#> 31  8.151878 0.6123296
#> 32  8.351878 0.5893782
#> 33  8.551878 0.5777416
#> 34  8.751878 0.5548858
#> 35  8.951878 0.5548858
#> 36  9.151878 0.5436819
#> 37  9.351878 0.5217708
#> 38  9.551878 0.5108233
#> 39  9.751878 0.4998672
#> 40  9.951878 0.4670233
#> 41 10.151878 0.4563563
#> 42 10.351878 0.4449410
#> 43 10.551878 0.4449410
#> 44 10.751878 0.4331449
#> 45 10.951878 0.4213851
#> 46 11.151878 0.4093684
#> 47 11.351878 0.4093684
#> 48 11.551878 0.4093684
#> 49 11.751878 0.4093684
#> 50 11.951878 0.4093684
#> 51 12.151878 0.4093684
#> 52 12.351878 0.4093684
#> 
##
  # }