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Essentially comparing:

glm1 = glmer(Mortality ~ CCI + PatientRace + PatientSex + age_cat + (CCI | FacilityIdentifier), 
             data = tmp, family = binomial, 
             control = glmerControl(optimizer = "bobyqa"), nAGQ = 1)

to

m1 = glm(Mortality ~ CCI + PatientRace + PatientSex + age_cat, 
         family=binomial, data = tmp)

To determine if the random effect is a significant contributor, hopefully to show that each facility doesn't have varying practices in measuring CCI that may affect interpretation of mortality. Would appreciate any advice.

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marked as duplicate by amoeba, kjetil b halvorsen, Peter Flom Mar 6 '18 at 12:08

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  • $\begingroup$ Why not just bootstrap confidence intervals and see if that of the random effect's variance includes zero (non-significant) or not (significant)? You can simply do this with confint(glm1, method = "boot"). $\endgroup$ – Frans Rodenburg Mar 6 '18 at 5:45
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    $\begingroup$ @amoeba woops, didn't mean to include that $\endgroup$ – Cenoc Mar 6 '18 at 10:13