simRecurrentII: Simulation of recurrent events data based on cumulative...

In kkholst/mets: Analysis of Multivariate Event Times

Simulation of recurrent events data based on cumulative hazards II

Description

Simulation of recurrent events data based on cumulative hazards

Usage

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

Author(s)

Thomas Scheike

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)

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)
par(mfrow=c(2,2))
showfitsim(causes=2,rr,dr,base1,base4)

kkholst/mets documentation built on April 3, 2024, 1:43 a.m.