[R] Conditional gap time frailty cox model for recurrent events
Therneau, Terry M., Ph.D.
therneau at mayo.edu
Tue Sep 6 15:56:15 CEST 2016
You can ignore the message below. The maximizing routine buried within the frailty()
command buried with coxph() has a maximizer that is not the brightest. It sometimes gets
lost but then finds its way again. The message is from one of those. It likely took a
not-so-good update step, and took a couple of iterations to recover.
In coxpenal.fit(X, Y, strats, offset, init = init, control, weights = weights, :
Inner loop failed to coverge for iterations 3 4
To be fair the maximizing problem for a mixed effects Cox model is not easy. In the coxme
code I have spent much more time on the details of this.
On 09/06/2016 05:00 AM, r-help-request at r-project.org wrote:
> Dear Elisabetta,
> I have no direct answer to your question, but a suggestion: Use the
> 'coxme' function (in the package with the same name). In the help page
> for 'frailty' (survival) you will find: "The coxme package has
> superseded this method. It is faster, more stable, and more flexible."
> Hth, G?ran
> On 2016-09-05 11:42, Elisabetta Petracci wrote:
>> >Dear users,
>> >I am fitting a conditional gap time frailty cox model weighting
>> >observations by means of inverse probability time dependent weigths.
>> >Attached find the self-explaining dataset.
>> >I have used the following sintax:
>> >And I get the following warning:
>> >Warning message:
>> >In coxpenal.fit(X, Y, strats, offset, init = init, control, weights =
>> >weights, :
>> > Inner loop failed to coverge for iterations 3 4
>> >I have tried to:
>> >- leave out the weights but I get the error anyway
>> >- to randomly select a subset of patients and I don't get the error. This
>> >seems to suggest that the problem is with some observations.
>> >Any suggestion?
>> >Many thanks,
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