Bootstrapped CI using FastJM
cisurvfitJMCS.RdBootstrapped CI for predicted survival probability
Value
Bootstrap CI for the survival probability and other relevant information for predicted survival plot
Examples
# \donttest{
##
library(survival)
#> Warning: package 'survival' was built under R version 4.3.3
library(dplyr)
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
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')
bootci<-cisurvfitJMCS(P2)
print(bootci)
#>
#> Predicted survival proability data
#> Times LL Med UL
#> 1 2.351878 1.0000000 1.0000000 1.0000000
#> 2 2.551878 0.9882157 0.9936621 1.0000000
#> 3 2.751878 0.9743832 0.9896088 0.9988270
#> 4 2.951878 0.9679743 0.9831477 0.9982269
#> 5 3.151878 0.9597492 0.9820571 0.9915386
#> 6 3.351878 0.9502830 0.9816114 0.9915386
#> 7 3.551878 0.9383326 0.9754304 0.9894134
#> 8 3.751878 0.9383326 0.9744906 0.9894134
#> 9 3.951878 0.9163845 0.9584968 0.9867460
#> 10 4.151878 0.9144916 0.9492577 0.9867460
#> 11 4.351878 0.9019256 0.9459809 0.9769904
#> 12 4.551878 0.8903315 0.9379571 0.9736214
#> 13 4.751878 0.8734849 0.9333069 0.9657754
#> 14 4.951878 0.8520834 0.9228264 0.9533681
#> 15 5.151878 0.8520834 0.9228264 0.9492620
#> 16 5.351878 0.8390992 0.9075731 0.9470251
#> 17 5.551878 0.8390992 0.8965544 0.9424155
#> 18 5.751878 0.8346794 0.8930066 0.9345108
#> 19 5.951878 0.8087215 0.8724983 0.9231169
#> 20 6.151878 0.7896375 0.8583634 0.9094319
#> 21 6.351878 0.7743486 0.8579572 0.9047730
#> 22 6.551878 0.7613223 0.8443480 0.9019735
#> 23 6.751878 0.7593086 0.8221628 0.8863317
#> 24 6.951878 0.7479515 0.8146577 0.8863317
#> 25 7.151878 0.7323177 0.8109213 0.8838719
#> 26 7.351878 0.7323177 0.8109213 0.8827124
#> 27 7.551878 0.7215099 0.7987750 0.8812711
#> 28 7.751878 0.7103620 0.7945143 0.8783955
#> 29 7.951878 0.6993335 0.7880355 0.8783955
#> 30 8.151878 0.6971499 0.7847947 0.8602679
#> 31 8.351878 0.6971499 0.7776118 0.8588124
#> 32 8.551878 0.6971499 0.7695602 0.8531060
#> 33 8.751878 0.6971499 0.7588306 0.8500690
#> 34 8.951878 0.6967911 0.7475618 0.8485322
#> 35 9.151878 0.6570295 0.7475618 0.8469767
#> 36 9.351878 0.6257875 0.7254336 0.8422764
#> 37 9.551878 0.5996068 0.6995193 0.8345402
#> 38 9.751878 0.5849632 0.6892104 0.8345402
#> 39 9.951878 0.5529481 0.6781402 0.8216601
#> 40 10.151878 0.5412047 0.6720234 0.8149544
#> 41 10.351878 0.5383874 0.6671475 0.8080734
#> 42 10.551878 0.5262847 0.6447058 0.8080734
#> 43 10.751878 0.5116144 0.6447058 0.7955063
#> 44 10.951878 0.5115575 0.6447058 0.7955063
#> 45 11.151878 0.4734098 0.6214168 0.7812288
#> 46 11.351878 0.4704322 0.6145357 0.7812288
#> 47 11.551878 0.4704322 0.6100594 0.7590442
#> 48 11.751878 0.4404278 0.6100594 0.7492311
#> 49 11.951878 0.4404278 0.6100594 0.7492311
#> 50 12.151878 0.4346091 0.5961206 0.7492311
#> 51 12.351878 0.4152544 0.5864073 0.7442066
##
# }