survProb.RdThis function calculates the baseline hazard, baseline cummulative hazard, at different unique event times
survProb(fitted.object, nimp = 1, conf_level = 0.95)model object fitted through JMtwostage
imputation number
confindence level
list with baseline hazard, baseline cummulative hazard.
library(survival)
model_wMI<- jmwMI(ldata=long_data,sdata=surv_data,
timeDep=c("marker_1","marker_2","marker_3"),
impModel=list(marker_1~Z_1+Z_2+Time+(1|ID),
marker_2~Z_1+Z_2+Time+(1|ID),
marker_3~Z_1+Z_2+Time+(1|ID)),
ipwModel=list(~Z_1+Time+(1|ID),
~Z_1+Time+(1|ID),
~Z_1+Time+(1|ID)),
visitTime="Time",
coxModel=Surv(survival_time,survival_status)~Z_1+Z_2+
td(marker_1)+td(marker_2)+td(marker_3),
model="Cox",id="ID")
#> boundary (singular) fit: see help('isSingular')
survProb(model_wMI,nimp=1)
#> $baseline_hazard
#> [1] 0.0002046426 0.0003090159 0.0003123678 0.0004206932 0.0004287858
#> [6] 0.0004327539 0.0004358643 0.0005644538 0.0005717323 0.0005804433
#> [11] 0.0005912090 0.0006032890 0.0006064799 0.0006141351 0.0006214323
#> [16] 0.0006318016 0.0006384437 0.0006408249 0.0006493254 0.0006555627
#> [21] 0.0006662782 0.0006984931 0.0007114846 0.0007200375 0.0007296993
#> [26] 0.0007333800 0.0007411339 0.0007559110 0.0007662730 0.0007912423
#> [31] 0.0008042524 0.0008145708 0.0008366339 0.0008562451 0.0008819969
#> [36] 0.0008946464 0.0009000183 0.0009120692 0.0009319080 0.0009624745
#> [41] 0.0009687046 0.0009826694 0.0009930884 0.0010132875 0.0010285320
#> [46] 0.0010416554 0.0010708203 0.0010925956 0.0011262698 0.0011475112
#> [51] 0.0012032557 0.0012216774 0.0012695170 0.0013096856 0.0013484852
#> [56] 0.0013761733 0.0013913026 0.0014195504 0.0014297854 0.0014756879
#> [61] 0.0015753162 0.0016062882 0.0017081642 0.0017696841 0.0018199725
#> [66] 0.0019430848 0.0019760902 0.0021196019 0.0023200935 0.0024021509
#> [71] 0.0025170977 0.0026058085 0.0026917414 0.0027611817 0.0028458680
#> [76] 0.0032654770 0.0037919777 0.0075786714 0.0098151182
#>
#> $cum_baseline_hazard
#> [1] 0.0002046426 0.0005136586 0.0008260264 0.0012467196 0.0016755053
#> [6] 0.0021082592 0.0025441235 0.0031085774 0.0036803097 0.0042607530
#> [11] 0.0048519620 0.0054552510 0.0060617309 0.0066758660 0.0072972983
#> [16] 0.0079290999 0.0085675436 0.0092083685 0.0098576939 0.0105132566
#> [21] 0.0111795348 0.0118780278 0.0125895125 0.0133095500 0.0140392493
#> [26] 0.0147726293 0.0155137633 0.0162696742 0.0170359473 0.0178271896
#> [31] 0.0186314420 0.0194460128 0.0202826467 0.0211388919 0.0220208887
#> [36] 0.0229155351 0.0238155534 0.0247276226 0.0256595306 0.0266220050
#> [41] 0.0275907097 0.0285733791 0.0295664674 0.0305797549 0.0316082869
#> [46] 0.0326499423 0.0337207626 0.0348133582 0.0359396280 0.0370871392
#> [51] 0.0382903949 0.0395120723 0.0407815894 0.0420912750 0.0434397602
#> [56] 0.0448159335 0.0462072361 0.0476267865 0.0490565720 0.0505322599
#> [61] 0.0521075761 0.0537138644 0.0554220285 0.0571917126 0.0590116851
#> [66] 0.0609547699 0.0629308601 0.0650504620 0.0673705555 0.0697727064
#> [71] 0.0722898041 0.0748956126 0.0775873540 0.0803485357 0.0831944038
#> [76] 0.0864598807 0.0902518584 0.0978305298 0.1076456480
#>
#> $unique_times
#> [1] 1.214073 2.220477 2.742172 3.110835 3.732530 3.822479 3.926222
#> [8] 4.305836 4.559980 4.700511 4.723295 4.869968 4.893231 4.914169
#> [15] 5.175517 5.341789 5.604338 5.627700 5.741271 6.355630 6.456136
#> [22] 6.488063 6.798677 6.925235 6.950811 6.997569 7.066035 7.240583
#> [29] 7.487975 7.715308 7.887401 7.916136 8.163059 8.255911 8.308563
#> [36] 8.455062 8.495823 8.509121 8.581039 8.641348 8.710770 8.720892
#> [43] 8.827812 8.836715 8.890152 8.940512 9.140512 9.156370 9.533873
#> [50] 9.561530 9.648077 9.882902 9.978393 10.140161 10.223021 10.225053
#> [57] 10.309753 10.335368 10.405831 10.468340 11.038877 11.139249 11.197101
#> [64] 11.235224 11.394633 11.495762 11.518071 11.589135 11.595206 11.687020
#> [71] 11.784571 12.300715 12.396805 12.408910 12.433343 12.504337 12.785246
#> [78] 13.157751 13.233371
#>