[R] Gaussian frailty leads to segmentation fault

[Christian Lederer]

Dear Thomas,

attached you find a data frame which produces the error.
I am using survival 2.11-5 under R 1.9.1-1 and 1.9.0-1.

By the way, if i randomly omit 50% of the data, i usually
get no crash, but a warning message like this:
Inner loop failed to coverge for iterations 1 2 3 in: coxpenal.fit(X, 
Y, strats, offset, init = init, control, weights = weights,

Maybe, the model is not appropriate for this kind of data.
But on the other hand, as soon the treatment group (study == 1, 
treatment == 1) is smaller than the randomized placebo group
(study == 1, treatment == 0), the warnings disappear.
and the model gives reasonable results in my first simulations
with normally distributed study effects.

Christian




Thomas Lumley wrote:
> 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
> 
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