mets (version 1.2.4)

simRecurrent: Simulation of recurrent events data based on cumulative hazards

Description

Simulation of recurrent events data based on cumulative hazards

Usage

simRecurrent(n, cumhaz, death.cumhaz = NULL, cumhaz2 = NULL,
  gap.time = FALSE, max.recurrent = 100, dhaz = NULL, haz2 = NULL,
  dependence = 0, var.z = 2, cor.mat = NULL, ...)

Arguments

n

number of id's

cumhaz

cumulative hazard of recurrent events

death.cumhaz

cumulative hazard of death

cumhaz2

cumulative hazard of recurrent events of type 2

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, first random effect is z1 and shared for a possible second cause, second random effect is for death

var.z

variance of random effects

cor.mat

correlation matrix for var.z variance of random effects

...

Additional arguments to lower level funtions

Details

Must give hazard of death and recurrent events. Possible with two event types and their dependence can be specified but the two recurrent events need to share random effect. combined to prducte the estimator

Examples

# NOT RUN {
########################################
## 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(1000,base1,death.cumhaz=dr)
 par(mfrow=c(1,3))
 showfitsim(causes=1,rr,dr,base1,base1)

######################################################################
### simulating simple model that mimicks data 
### now with two event types and second type has same rate as death rate
######################################################################

 rr <- simRecurrent(1000,base1,death.cumhaz=dr,cumhaz2=base4)
 dtable(rr,~death+status)
 par(mfrow=c(2,2))
 showfitsim(causes=2,rr,dr,base1,base4)

######################################################################
### simulating simple model 
### random effect for all causes (Z shared for death and recurrent) 
######################################################################

 rr <- simRecurrent(1000,base1,
        death.cumhaz=dr,dependence=1,var.gamma=0.4)
 ### marginals do fit after input after integrating out
 par(mfrow=c(2,2))
 showfitsim(causes=1,rr,dr,base1,base1)

# }