mets (version 1.3.4)

simMultistate: Simulation of illness-death model

Description

Simulation of illness-death model

Usage

simMultistate(
  n,
  cumhaz,
  cumhaz2,
  death.cumhaz,
  death.cumhaz2,
  rr = NULL,
  rr2 = NULL,
  rd = NULL,
  rd2 = NULL,
  gap.time = FALSE,
  max.recurrent = 100,
  dependence = 0,
  var.z = 0.22,
  cor.mat = NULL,
  cens = NULL,
  ...
)

Arguments

n

number of id's

cumhaz

cumulative hazard of going from state 1 to 2.

cumhaz2

cumulative hazard of going from state 2 to 1.

death.cumhaz

cumulative hazard of death from state 1.

death.cumhaz2

cumulative hazard of death from state 2.

rr

relative risk adjustment for cumhaz

rr2

relative risk adjustment for cumhaz2

rd

relative risk adjustment for death.cumhaz

rd2

relative risk adjustment for death.cumhaz2

gap.time

if true simulates gap-times with specified cumulative hazard

max.recurrent

limits number recurrent events to 100

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

Author

Thomas Scheike

Details

simMultistate with different death intensities from states 1 and 2

Must give cumulative hazards on some time-range

Examples

########################################
## getting some rates to mimick 
########################################
data(base1cumhaz)
data(base4cumhaz)
data(drcumhaz)
dr <- drcumhaz
dr2 <- drcumhaz
dr2[,2] <- 1.5*drcumhaz[,2]
base1 <- base1cumhaz
base4 <- base4cumhaz
cens <- rbind(c(0,0),c(2000,0.5),c(5110,3))

iddata <- simMultistate(10000,base1,base1,dr,dr2,cens=cens)
dlist(iddata,.~id|id<3,n=0)
 
### estimating rates from simulated data  
c0 <- phreg(Surv(start,stop,status==0)~+1,iddata)
c3 <- phreg(Surv(start,stop,status==3)~+strata(from),iddata)
c1 <- phreg(Surv(start,stop,status==1)~+1,subset(iddata,from==2))
c2 <- phreg(Surv(start,stop,status==2)~+1,subset(iddata,from==1))
###
par(mfrow=c(2,3))
bplot(c0)
lines(cens,col=2) 
bplot(c3,main="rates 1-> 3 , 2->3")
lines(dr,col=1,lwd=2)
lines(dr2,col=2,lwd=2)
###
bplot(c1,main="rate 1->2")
lines(base1,lwd=2)
###
bplot(c2,main="rate 2->1")
lines(base1,lwd=2)