Simulation of recurrent events data based on cumulative hazards II
Source:R/recurrent.marginal.R
simRecurrentII.RdSimulation of recurrent events data based on cumulative hazards
simRecurrentII(
n,
cumhaz,
cumhaz2,
death.cumhaz = NULL,
r1 = NULL,
r2 = NULL,
rd = NULL,
rc = NULL,
gap.time = FALSE,
max.recurrent = 100,
dhaz = NULL,
haz2 = NULL,
dependence = 0,
var.z = 0.22,
cor.mat = NULL,
cens = NULL,
...
)Arguments
- n
number of id's
- cumhaz
cumulative hazard of recurrent events
- cumhaz2
cumulative hazard of recurrent events of type 2
- death.cumhaz
cumulative hazard of death
- r1
potential relative risk adjustment of rate
- r2
potential relative risk adjustment of rate
- rd
potential relative risk adjustment of rate
- rc
potential relative risk adjustment of rate
- gap.time
if true simulates gap-times with specified cumulative hazard
- max.recurrent
limits number recurrent events to 100
- dhaz
rate for death hazard if it is extended to time-range of first event
- haz2
rate of second cause if it is extended to time-range of first event
- dependence
0:independence; 1:all share same random effect with variance var.z; 2:random effect exp(normal) with correlation structure from cor.mat; 3:additive gamma distributed random effects, z1= (z11+ z12)/2 such that mean is 1 , z2= (z11^cor.mat(1,2)+ z13)/2, z3= (z12^(cor.mat(2,3)+z13^cor.mat(1,3))/2, with z11 z12 z13 are gamma with mean and variance 1 , first random effect is z1 and for N1 second random effect is z2 and for N2 third random effect is for death
- var.z
variance of random effects
- cor.mat
correlation matrix for var.z variance of random effects
- cens
rate of censoring exponential distribution
- ...
Additional arguments to lower level funtions
Details
Must give hazard of death and two recurrent events. Possible with two event types and their dependence can be specified but the two recurrent events need to share random effect. Based on drawing the from cumhaz and cumhaz2 and taking the first event rather the cumulative and then distributing it out. Key advantage of this is that there is more flexibility wrt random effects
Examples
########################################
## getting some rates to mimick
########################################
data(base1cumhaz)
data(base4cumhaz)
data(drcumhaz)
dr <- drcumhaz
base1 <- base1cumhaz
base4 <- base4cumhaz
cor.mat <- corM <- rbind(c(1.0, 0.6, 0.9), c(0.6, 1.0, 0.5), c(0.9, 0.5, 1.0))
######################################################################
### simulating simple model that mimicks data
######################################################################
rr <- simRecurrent(5,base1,death.cumhaz=dr)
dlist(rr,.~id,n=0)
#> id: 1
#> entry time status rr rr2 dtime fdeath death start stop
#> 1 0.0000 136.1547 1 1 1 4228.637 1 0 0.0000 136.1547
#> 6 136.1547 1067.9961 1 1 1 4228.637 1 0 136.1547 1067.9961
#> 11 1067.9961 1637.9417 1 1 1 4228.637 1 0 1067.9961 1637.9417
#> 16 1637.9417 2063.6863 1 1 1 4228.637 1 0 1637.9417 2063.6863
#> 20 2063.6863 2265.6707 1 1 1 4228.637 1 0 2063.6863 2265.6707
#> 24 2265.6707 2686.8992 1 1 1 4228.637 1 0 2265.6707 2686.8992
#> 27 2686.8992 3554.8018 1 1 1 4228.637 1 0 2686.8992 3554.8018
#> 29 3554.8018 4228.6367 0 1 1 4228.637 1 1 3554.8018 4228.6367
#> ------------------------------------------------------------
#> id: 2
#> entry time status rr rr2 dtime fdeath death start stop
#> 2 0.00000 46.29699 1 1 1 5110 0 0 0.00000 46.29699
#> 7 46.29699 251.49166 1 1 1 5110 0 0 46.29699 251.49166
#> 12 251.49166 445.20831 1 1 1 5110 0 0 251.49166 445.20831
#> 17 445.20831 1216.85609 1 1 1 5110 0 0 445.20831 1216.85609
#> 21 1216.85609 1500.60616 1 1 1 5110 0 0 1216.85609 1500.60616
#> 25 1500.60616 2514.41422 1 1 1 5110 0 0 1500.60616 2514.41422
#> 28 2514.41422 2577.52247 1 1 1 5110 0 0 2514.41422 2577.52247
#> 30 2577.52247 2984.54042 1 1 1 5110 0 0 2577.52247 2984.54042
#> 31 2984.54042 3990.51506 1 1 1 5110 0 0 2984.54042 3990.51506
#> 32 3990.51506 4726.57351 1 1 1 5110 0 0 3990.51506 4726.57351
#> 33 4726.57351 5110.00000 0 1 1 5110 0 0 4726.57351 5110.00000
#> ------------------------------------------------------------
#> id: 3
#> entry time status rr rr2 dtime fdeath death start stop
#> 3 0.000 1353.162 1 1 1 1472.609 1 0 0.000 1353.162
#> 8 1353.162 1435.495 1 1 1 1472.609 1 0 1353.162 1435.495
#> 13 1435.495 1472.609 0 1 1 1472.609 1 1 1435.495 1472.609
#> ------------------------------------------------------------
#> id: 4
#> entry time status rr rr2 dtime fdeath death start
#> 4 0.00000 82.54944 1 1 1 2007.163 1 0 0.00000
#> 9 82.54944 140.71176 1 1 1 2007.163 1 0 82.54944
#> 14 140.71176 156.85523 1 1 1 2007.163 1 0 140.71176
#> 18 156.85523 520.76443 1 1 1 2007.163 1 0 156.85523
#> 22 520.76443 1890.81079 1 1 1 2007.163 1 0 520.76443
#> 26 1890.81079 2007.16317 0 1 1 2007.163 1 1 1890.81079
#> stop
#> 4 82.54944
#> 9 140.71176
#> 14 156.85523
#> 18 520.76443
#> 22 1890.81079
#> 26 2007.16317
#> ------------------------------------------------------------
#> id: 5
#> entry time status rr rr2 dtime fdeath death start stop
#> 5 0.0000 407.6795 1 1 1 1726.288 1 0 0.0000 407.6795
#> 10 407.6795 688.8398 1 1 1 1726.288 1 0 407.6795 688.8398
#> 15 688.8398 836.1697 1 1 1 1726.288 1 0 688.8398 836.1697
#> 19 836.1697 1420.3291 1 1 1 1726.288 1 0 836.1697 1420.3291
#> 23 1420.3291 1726.2880 0 1 1 1726.288 1 1 1420.3291 1726.2880
rr <- simRecurrent(100,base1,death.cumhaz=dr)
par(mfrow=c(1,3))
showfitsim(causes=1,rr,dr,base1,base1)
######################################################################
### simulating simple model
### random effect for all causes (Z shared for death and recurrent)
######################################################################
rr <- simRecurrent(100,base1,death.cumhaz=dr,dependence=1,var.gamma=0.4)
######################################################################
### simulating simple model that mimicks data
### now with two event types and second type has same rate as death rate
######################################################################
set.seed(100)
rr <- simRecurrentII(100,base1,base4,death.cumhaz=dr)
dtable(rr,~death+status)
#>
#> status 0 1 2
#> death
#> 0 10 295 39
#> 1 90 0 0
par(mfrow=c(2,2))
showfitsim(causes=2,rr,dr,base1,base4)
