[R] coxme: model simplification using LR-test?

Simon Tragust
Sun, 19 Feb 2012 15:45:04 -0000

Hi
I'm encountering some problems with coxme

My data:
I'm looking at the survival of animals in an experiment with 3 treatments,
which came from 4 different populations, two of which were infected with a
parasite and two of which were not. I'm interested if infected animals
differe from uninfected ones across treatments.

Factor 1: treatment (3 levels)
Factor 2: infection state (infected/uninfected)
Random effect 1: (population nested within infection state)

modelling this with
m<-coxme(Surv(day,status)~condition*infection+(1|infection/population),data=all)

gives me the following

Cox mixed-effects model fit by maximum likelihood
  Data: all
  events, n = 476, 720
  Iterations= 7 53
                    NULL Integrated    Fitted
Log-likelihood -2915.527  -2641.427 -2634.182

                   Chisq   df p    AIC    BIC
Integrated loglik 548.20 7.00 0 534.20 505.04
 Penalized loglik 562.69 6.96 0 548.78 519.80

Model:  Surv(day, status) ~ condition * infestation + (1 |
infestation/population1)
Fixed coefficients
                                                 coef  exp(coef)  se(coef)     z
p
conditionstarved                            3.3960657 29.8464431 0.3228277 10.52
0.0000
conditionwater                              3.3277968 27.8768547 0.3224368 10.32
0.0000
infestationinfestationyes                   1.5596539  4.7571747 0.7254405  2.15
0.0320
conditionstarved:infestationinfestationyes -1.1100987  0.3295264 0.3712690 -2.99
0.0028
conditionwater:infestationinfestationyes   -0.9150922  0.4004797 0.3709914 -2.47
0.0140

Random effects
 Group                   Variable    Std Dev      Variance
 infestation/population1 (Intercept) 0.6367618042 0.4054655953
 infestation             (Intercept) 0.0199767654 0.0003990712


To assess if the interaction is needed I would normally do a model
simplification

m1<-update(m,~.-condition:infection)

however, this gives me

error in formula.default(object, env = baseenv()) : invalid formula

I do not encounter this problem without a random effect in coxph. So my question
is

(1)Is it not possible to do model simplification with coxme?
(2)Is there another way to assess an overall significant interaction with coxme?

Thanks in advance
Simon
Simon Tragust
Animal Ecology I
NW I
University of Bayreuth
D-95440 Bayreuth