R help - Nov 2012 - Multiple imputed data and variable selection / significance

Hello all,

I used the MICE procedure (of package "mice") to impute a dataset  (I
got m imputed datasets). Now I would like to fit a GLM with a poisson
error distribution to regress a count variable on 14 continuous
predictor variables and test for the significance of the different
predictors by means of likelihood ratio tests (LRT).

I can estimate the pooled estimates and respective standard errors (of
the m fitted GLMs) using function "pool" (which follows Rubin's
1987
rules). However, as far as I understand, the function "pool.compare"
only allows to perform LRT on logistic models (presumably model with a
binomial error distribution). Is this correct, or can I use this
function to perform the a LRT for poisson models?

Otherwise, would it be correct to average the residual deviance across
the m fits for both the full and nested (in which the variable for
testing is removed) models to posteriorly perform the chi-square test
(difference of averaged deviance, difference of df)?

Any help would be greatly appreciated.

Duarte