On 2014-03-24 07:22, David Winsemius wrote:
On Mar 23, 2014, at 9:14 PM, Lucy Leigh wrote:
Hi everyone,
I am currently attempting to simulate some survival data, based on
a Weibull model. I basically need to simulate some survival data so
that I can then test out a program I'm writing, before applying it
to some real data.
I've managed to create failure time data, using the rweibull
function. I.e. Y[i] <- rweibull(1, shape, scale) For a variety of
shape and scale parameters etc.
I wish to add in right censoring. If I am considering my failure
times as deaths, and the censoring time as a study cut-off date
(for example, 5 years from baseline), would it be correct to do
something like,
for(i in 1:nSubjects){ if (Y[i] > 5){ Y[i] <- 5} else {Y[i] <-
Y[i] }}
This would be more R-ish if you did it this way (loop-free)
Y[ Y > 5 ] <- 5 # set cutoff
cens.time <- runif(length(Y), 0, max(Y))
cens <- cens.time < Y
Not really; Given survival times T and censoring times C ( == 5 in
Lucy's example):
Y <- pmin(Y, C)
event <- (Y <= C)
gives what Lucy wants, with Y being the censored times and 'event'
indicating an observed death.
Göran
survreg( Surv(Y, !cens) ~ 1 )
Untested (well, lightly tested) in absence of data object.
I guess my question is, is it statistically sound to impose the
right censoring after estimating the distribution with no
censoring? I am leaning towards yes, because in an ideal world
where we followed all participants until they died, the
distribution would be as estimated by the rweibull above....and
assuming that the right censoring is independent, then cutting of
the measurements at a pre-specified time wouldn't affect the
outcome at all. But I guess I am just looking for a second
opinion?
Thanks in advance, this list has been immensely helpful with some
previous questions I have had about the survival package. Lucy
Please post in plain text.
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