[R-meta] multilevel glmm meta-analysis question

Kayleigh Chalkowski kzc0061 @end|ng |rom @uburn@edu
Tue Mar 2 20:30:18 CET 2021

Dear all,

I am undertaking a multilevel generalized linear mixed model meta-analysis and, following the advice here<http://www.metafor-project.org/doku.php/todo> regarding multilevel glmms in metafor, I am using the glmer function of the lme4 package. I am wondering 1) if that I am specifying my weights correctly and 2) how to get values that are important to report for this meta-analysis, like heterogeneity.

In this meta-analysis, I hypothesize that both socioeconomic and ecological variables are important in predicting parasite prevalence in free-roaming cats and dogs-- so I'm mainly interested in the effects of moderators (an average prevalence isn't very informative). Since each study gives a proportion of dogs/cats infected out of a total, I have chosen a binomial model.

Here is my code for one of my univariable models:
res_san_3<-glmer(prevalence ~ san_10 + (1 | Species) + (1 | country) + (1 | study) + (1 | uniq), weights = 1/vi, data=feral, family=binomial, na.action=na.fail)

prevalence is the total infected out of the total sampled dogs/cats of each study, and each listed random effect there are the different nested levels. Nested levels include species of parasite, followed by country, then study, then each sample within each study (because many studies sampled multiple parasites). I used inverse variance for the weights here.

I would greatly appreciate any thoughts, or any helpful information that could be referred to me. I've searched the web extensively for help understanding multilevel glmms and was unable to find answers to my questions.

Thank you so much,

Below here is the output from that model in case it is helpful:

Generalized linear mixed model fit by maximum likelihood (Laplace Approximation) ['glmerMod']
 Family: binomial  ( logit )
Formula: prevalence ~ san_10 + (1 | Species) + (1 | country) + (1 | study) +      (1 | uniq)
   Data: feral
Weights: 1/vi

     AIC      BIC   logLik deviance df.resid
  5131.5   5162.8  -2559.7   5119.5     1374

Scaled residuals:
    Min      1Q  Median      3Q     Max
-1.3271 -0.4905 -0.2745  0.1395  1.8286

Random effects:
 Groups  Name        Variance Std.Dev.
 uniq    (Intercept) 0.650879 0.80677
 study   (Intercept) 0.386573 0.62175
 Species (Intercept) 0.245294 0.49527
 country (Intercept) 0.007934 0.08907
Number of obs: 1380, groups:  uniq, 1380; study, 449; Species, 204; country, 70

Fixed effects:
            Estimate Std. Error z value Pr(>|z|)
(Intercept) -0.49574    0.29256  -1.694 0.090176 .
san_10      -0.11641    0.03191  -3.648 0.000264 ***
Signif. codes:  0 �***� 0.001 �**� 0.01 �*� 0.05 �.� 0.1 � � 1

Kayleigh Chalkowski, M.Sc.

PhD Student

Fulbright Madagascar 2020-2021

School of Forestry and Wildlife Sciences

Auburn University

(607) 319-6342

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