mets (version 1.3.11)

rcrisk: Simulation of Piecewise constant hazard models with two causes (Cox).

Description

Simulates data from piecwise constant baseline hazard that can also be of Cox type. Censor data at highest value of the break points for either of the cumulatives, see also sim_phregs

Usage

rcrisk(
  cumA,
  cumB,
  rr1 = NULL,
  rr2 = NULL,
  n = NULL,
  cens = NULL,
  rrc = NULL,
  extend = TRUE,
  causes = NULL,
  ...
)

Arguments

cumA

cumulative hazard of cause 1, or list of multiple cumulative hazards

cumB

cumulative hazard of cause 2 or NULL when cumA is a list

rr1

number of simulations or vector of relative risk for simuations, or matrix with columns equal to number of hazards in list

rr2

number of simulations or vector of relative risk for simuations.

n

number of simulation if rr not given, must be given when rr is not given

cens

to censor further , rate or cumumlative hazard

rrc

retlativ risk for censoring.

extend

to extend the cumulative hazards to largest end-point

causes

to assign status values for each of the causes, vector of integers

...

arguments for rchaz

Author

Thomas Scheike

Examples

data(bmt); 
n <- 100
cox1 <- phreg(Surv(time,cause==1)~tcell+platelet,data=bmt)
cox2 <- phreg(Surv(time,cause==2)~tcell+platelet,data=bmt)

X1 <- bmt[,c("tcell","platelet")]
xid <- sample(1:nrow(X1),n,replace=TRUE)
Z1 <- X1[xid,]
Z2 <- X1[xid,]
rr1 <- exp(as.matrix(Z1) %*% cox1$coef)
rr2 <- exp(as.matrix(Z2) %*% cox2$coef)

d <-  rcrisk(cox1$cum,cox2$cum,rr1,rr2,cens=2/70)
dd <- cbind(d,Z1)

d <-  rcrisk(cox1$cum,cox2$cum,rr1,rr2,cens=cbind(c(1,30,68),c(.01,1,3)))
dd <- cbind(d,Z1)

par(mfrow=c(1,3))
scox0 <- phreg(Surv(time,status==0)~tcell+platelet,data=dd)
plot(scox0); lines(cbind(c(1,30,68),c(.01,1,3)),col=2)
##
scox1 <- phreg(Surv(time,status==1)~tcell+platelet,data=dd)
scox2 <- phreg(Surv(time,status==2)~tcell+platelet,data=dd)
plot(cox1); plot(scox1,add=TRUE,col=2)
plot(cox2); plot(scox2,add=TRUE,col=2)
cbind(cox1$coef,scox1$coef,cox2$coef,scox2$coef)

# 3 causes and censoring 
d3 <-  rcrisk(list(cox1$cum,cox2$cum,cox1$cum),NULL,n=100,cens=cbind(c(1,30,68),c(.01,1,3)))
dtable(d3,~status)