Hi,

I'm actually trying to rank a set of candidate models with an information criterion (AICc, QIC, BIC). The problem I have is that I use mixed-effect cox regression only available with the package {coxme} (see the example below).

#Model1
>spring.cox <- coxme (Surv(start, stop, Real_rand) ~ strata(Paired)+R4+R3+R2+(R3|Individual), spring)


I've already found some explications in this forum to adjust QIC on coxph object (see the following lines, thanks to M. Basille), but it doesn't work on coxme...

>QIC <- function(mod, ...)
>    UseMethod("QIC")
>
>QIC.coxph <- function(spring.cox, details = FALSE) {
>    trace <- sum(diag(solve(spring.cox$naive.var) %*% spring.cox$var))
>    quasi <- spring.cox$loglik[2]
>    return(-2*quasi + 2*trace)
>}

The only thing that I can't find in the coxme output to use these previous commands is the "naive.var", that we can obtain in coxph regression by specifying "robust=TRUE" in the argument list:

>spring.cox <- coxph (Surv(start, stop, Real_rand) ~ strata(Paired)+R4+R3+R2, spring, robust=T)


But coxph doesn't allow inclusion of interaction between two random variables (R3|Individual), and it's why I have to use coxme.
I found a new update in R-forge to improve {coxme} (r-forge.r-project.org/scm/viewvc.php/pkg/R/dredge.R?view=log&root=mumin), but I did not understand all it works and I'm not sure it fixes my problems...

Is there someone that can help me with that?

Rémi Lesmerises, biol. M.Sc.,

Candidat Ph.D. en Biologie
Université du Québec à Rimouski
300, allée des Ursulines
Rimouski, Qc., G5L 3A1
Tél.: 1 800 511-3382  #1241
remilesmerises@yahoo.ca
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