[R] Gaussian frailty leads to segmentation fault

[Thomas Lumley]

We really need a reproducible example to find segmentation faults.  Can
you make one?

	-thomas


On Wed, 28 Jul 2004, Christian Lederer wrote:

>
> Dear R gurus,
>
> for a simulation concerning study effects and historical controls
> in survival analysis, i would like to experiment with a gaussian
> frailty model.
>
> The simulated scenario consists of a randomized trial
> (treatment and placebo) and historical controls (only placebo).
>
> So the simulated data frames consist of four columns
> $time, $cens, $study, $treat.
> $time, $cens are the usual survival data.
> For the binary thretment indicator we have
> $treat == 0 or 1, if $study == 1,
> $treat == 1 if $study > 1
>
> Typical parameters for my simulations are:
> sample sizes (per arm):         between 100 and 200
> number of historical studies:   between 7 and 15
> hazard ratio treatment/placebo: between 0.7 and 1
> variance of the study effekt:   between 0 and 0.3
>
> Depending on the sample sizes, the following call sometimes leads to
> a segmentation fault:
>
> coxph(Surv(time,cens) ~
>        as.factor(treatment) + frailty(study, distribution="gaussian"),
>        data=data)
>
> I noticed, that this segmentation fault occures most frequently, if the
> number of randomized treatment patients is higher than the number of
> randomized placebo patients, and the number of historical studies is
> large.
> There seems to be no problem, if there are at least as many randomized
> placebo patients as treated patients. Unfortunately, this is not the
> situation i want to investigate (historical controls should be used
> to decrease the number of treated patients).
>
> Is there a way to circumwent this problem?
>
> Christian
>
> P.S.
> Is it allowed, to attach gzipped sample data sets in this mailing list?
>
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Thomas Lumley			Assoc. Professor, Biostatistics
tlumley at u.washington.edu	University of Washington, Seattle