[R-sig-Epi] Simulating a Weibull regression model

[Mark Clements]

>From ?survival::survreg.distributions, we find that the CDF for an extreme value distribution is defined as F=1-e^{-e^t}, whereas ?extRemes::revd gives G(x) = exp(-exp((x - location)/scale)) (or F=e^{-e^t}) when shape=0.

If you use

time<-exp(b0+b1*x1+b2*x2-c*error) # or
time<-rexp(n, rate=1/exp(b0+b1*x1+b2*x2)) # or
time<-rweibull(n, shape=c, scale=exp(b0+b1*x1+b2*x2))

then you could model using

survreg(Surv(time, status==1) ~ x1+x2,dist="exponential") # or
survreg(Surv(time, status==1) ~ x1+x2,dist="weibull")

-- Mark
________________________________
From: R-sig-Epi <r-sig-epi-bounces using r-project.org> on behalf of duo wan <duo_wan using yahoo.com>
Sent: 11 October 2022 20:05
To: r-sig-epi using r-project.org <r-sig-epi using r-project.org>
Subject: [R-sig-Epi] Simulating a Weibull regression model

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HI All,I am trying to simulate a AFT Weibull model in R using a log-linear mod
log(Y)=�0+�1X1+�2X2+cW


Where W is a extreme value distribution. My R code is below. I can not understand why the intercept estimate is 2.94, not lose to 1?  Thanks,
Vincent

install.packages("extRemes")
library(extRemes)

n<-500000
x1<-rnorm(n,0,1)
x2<-rnorm(n,0,1)
error<-revd(n, loc = 0, scale = 1)
b0<- 1
b1<-0.5
b2<--1.2
c<-1
time<-exp(b0+b1*x1+b2*x2+c*error)
status<-rep(1,n)
survreg(Surv(time, status==1) ~ x1+x2,dist="exponential")

####output

Coefficients:
(Intercept)          x1          x2

  2.9390956   0.4931199  -1.2061369

Scale fixed at 1
#####




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