Hi all,

I'm trying to generate a Weibull distribution including four covariates in
the model. Here is the code I used:

T = rweibull(200, shape=1.3,
scale=0.004*exp(-(-2.5*b1+2.5*b2+0.9*x1-1.3*x2)/1.3))
C = rweibull(n, shape=1.5, scale=0.008)   #censoring time
time = pmin(T,C)  #observed time is min of censored and true
event = time==T   # set to 1 if event is observed
return(cbind(time,event,T,C))

And then, I used this dataset to fit a Weibull regression mode:

survreg(Surv(time, event)~b1+b2+x1+x2, dist='weibull')

The result I got was:
Intercept       b1         b2         x1          x2       Scale
-3.983       1.916    -1.828    -0.808    1.022       0.778

I transformed those estimates into:

Shape = 1/0.778=1.3
beta1 = -1.916/0.778 = -2.5
beta2 = -(-1.828)/0.778 = 2.5
beta3 = -(-0.808)/0.778 = 1.0
beta4 = -1.022/0.778 = -1.3
scale =  exp(-3.983) = 0.0186

>From the result, we can see all parameter estimates are close to the
initial values, except for scale. The baseline scale is 0.004.
The scale parameter is varying by covariates.
If so, how can I double check if the estimated baseline scale is close to
the initial baseline scale?
Please advise if my concept or code is not correct.

Thank you very much.

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