# rchaz: Simulation of Piecewise constant hazard model (Cox). In timereg: Flexible Regression Models for Survival Data

 rchaz R Documentation

## Simulation of Piecewise constant hazard model (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.

### Usage

```rchaz(
cumhazard,
rr,
n = NULL,
entry = NULL,
cum.hazard = TRUE,
cause = 1,
extend = FALSE
)
```

### Arguments

 `cumhazard` cumulative hazard, or piece-constant rates for periods defined by first column of input. `rr` relative risk for simulations, alternatively when rr=1 specify n `n` number of simulation if rr not given `entry` delayed entry time for simuations. `cum.hazard` specifies wheter input is cumulative hazard or rates. `cause` name of cause `extend` to extend piecewise constant with constant rate. Default is average rate over time from cumulative (when TRUE), if numeric then uses given rate.

### Details

For a piecewise linear cumulative hazard the inverse is easy to compute with and delayed entry x we compute

Λ^{-1}(Λ(x) + E/RR)

, where RR are the relative risks and E is exponential with mean 1. This quantity has survival function

P(T > t | T>x) = exp(-RR (Λ(t) - Λ(x)))

.

Thomas Scheike

### Examples

```
rates <-  c(0,0.01,0.052,0.01,0.04)
breaks <- c(0,10,   20,  30,   40)
haz <- cbind(breaks,rates)
n <- 1000
X <- rbinom(n,1,0.5)
beta <- 0.2
rrcox <- exp(X * beta)
cumhaz <- cumsum(c(0,diff(breaks)*rates[-1]))
cumhaz <- cbind(breaks,cumhaz)

pctime <- rchaz(haz,n=1000,cum.hazard=FALSE)

par(mfrow=c(1,2))
ss <- aalen(Surv(time,status)~+1,data=pctime,robust=0)
plot(ss)
lines(cumhaz,col=2,lwd=2)

pctimecox <- rchaz(cumhaz,rrcox)
pctime <- cbind(pctime,X)
ssx <- cox.aalen(Surv(time,status)~+prop(X),data=pctimecox,robust=0)
plot(ssx)
lines(cumhaz,col=2,lwd=2)

### simulating data with hazard as real data
data(TRACE)

par(mfrow=c(1,2))
ss <- cox.aalen(Surv(time,status==9)~+prop(vf),data=TRACE,robust=0)
par(mfrow=c(1,2))
plot(ss)
###
pctime <- rchaz(ss\$cum,n=1000)
###
sss <- aalen(Surv(time,status)~+1,data=pctime,robust=0)
lines(sss\$cum,col=2,lwd=2)

pctime <- rchaz(ss\$cum,rrcox)
pctime <- cbind(pctime,X)
###
sss <- cox.aalen(Surv(time,status)~+prop(X),data=pctime,robust=0)
summary(sss)
plot(ss)
lines(sss\$cum,col=3,lwd=3)

```

timereg documentation built on Nov. 10, 2022, 5:48 p.m.