2
$\begingroup$

I aim at estimating a competing risks / multistate model with frailties with the coxme R-package and the survival package. I think that this is possible to do in the surivial package with the coxph function:

# competing risks model
survival::coxph(Surv(time, type) ~ X + frailty(ID), id = ID, data = data)  
# where type is a factor consisting of target states

# multistate model
survival::coxph(Surv(StartTime, EndTime, toState) ~ X, data = data, id = ID, state = fromState)

However, for the multistate model, it is not possible to add the frailty(ID) part of the formula. Why?

And: how would I go about to model competing risks and multistate models with the coxme package? The surivial package documentation (p. 48) suggests that the coxme package superseds the method of adding a frailty(ID) to the coxph term. Hence, it would be better to model frailties with the coxme package. But there is no documentation for the coxme package for modeling competing risks or multi-state models.

Improve this question
$\endgroup$
1
  • $\begingroup$ For what it's worth, I'm experiencing the same issue with frailty(ID), whereas cluster(ID) runs just fine. (Though I am not confident that cluster(ID) is correct!) $\endgroup$
    – carbocation
    Commented Oct 9, 2021 at 21:53

2 Answers 2

Reset to default
2
$\begingroup$

I cannot give you a definitive answer, but I faced a very similar problem with a multistate model. Likewise, I could not make frailty(ID) work properly with coxph. The model took an enormous amount of time to run. It might be a technical problem, as suggested in the 2015 paper you cite: "Only for ordinary survival models frailties can be fitted using standard software" (p.690).

Also, cluster(ID) becomes redundant if there is a univocal ID per transition (models with and without it shuold return the same estimates).

Conversely, coxme runs very smoothly. I think you can simply use the coxme package on an appropriately structured dataset (as you would with coxph). I found this guided example quite informative to begin with. coxme is also used in this paper to estimate a multistate model with frailty.

Improve this answer
$\endgroup$
3
  • $\begingroup$ Thanks a lot for your help. I think the guided example is technically not for a multistate model, as there is only one type of event (death). But the example paper is, and I think they also use a mix of functions from the survival and coxme pacakge. $\endgroup$
    – teeglaze
    Commented Aug 12, 2021 at 9:45
  • $\begingroup$ Yes, the worked example is not on multistate models; it just gives a nice, practical overview on frailty models with both coxph and coxme. From my reading of the paper, the authors use both packages to run two frailty models, which are then compared. Applying the same logic used with coxph to coxme, I think you can run a command line such as: coxme(Surv(StartTime, EndTime, toState==to) ~ X + (1 | ID), data = subset(data, fromState==from)). $\endgroup$
    – Alessandro
    Commented Aug 12, 2021 at 19:32
  • $\begingroup$ Ah, nice! Thanks for the formula. I did not know you could do it like that :) $\endgroup$
    – teeglaze
    Commented Aug 16, 2021 at 8:03
1
$\begingroup$

I don't have experience with frailty models, so I can't speak to why a frailty term seems to be incompatible with a multi-state model.

If you are primarily interested in accounting for intra-individual correlations and aren't wedded to the particulars of frailty modeling, however, then you can accomplish something similar with a cluster(id) term instead. With the cluster approach, the initial analysis is done without regard to the id values, but error estimates take the within-individual correlations into account. The idea is that a frailty term is like using a mixed model, while a cluster term is like using a generalized estimating equation.

Improve this answer
$\endgroup$
1
  • 1
    $\begingroup$ Thank you! I believe that the 'coxph' function already takes within-individual correlations into account when the argument 'id' has a value. So I guess the 'cluster(id)' term then becomes obsolete. I am still wondering why a frailty value for each id cannot be estimated (such as with 'frailty(ID)'). Is it a technical / implementation issue or a statistical one (also see this article). $\endgroup$
    – teeglaze
    Commented Mar 5, 2021 at 10:37

Your Answer

By clicking “Post Your Answer”, you agree to our terms of service and acknowledge you have read our privacy policy.

Not the answer you're looking for? Browse other questions tagged or ask your own question.