On Mar 6, 2012, at 5:53 PM, FU-WEN LIANG wrote:
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 read the read the help() for 'survreg' and make sure you follow
the links provided and work through the examples.
Please advise if my concept
The concept is the problem.
or code is not correct.
Thank you very much.
--
David Winsemius, MD
West Hartford, CT
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