Re: [R] Gaussian frailty leads to segmentation fault

From: Christian Lederer <>
Date: Thu 29 Jul 2004 - 13:51:23 EST

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:, 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.


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
>>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?
>>Is it allowed, to attach gzipped sample data sets in this mailing list?
>> mailing list
>>PLEASE do read the posting guide!
> Thomas Lumley			Assoc. Professor, Biostatistics
>	University of Washington, Seattle
> ______________________________________________
> mailing list
> PLEASE do read the posting guide!

______________________________________________ mailing list PLEASE do read the posting guide! Received on Thu Jul 29 11:58:14 2004

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