How to interpret significant test of moderators and null percentage of explained variance in meta-analysis using "metafor" package?

Question

I'm currently working on a meta-analysis of proportions (number of mosquitoes transmitting a disease/number of mosquitoes tested), using metafor package (Viechtbauer, 2010). My aim is to compute a summarized effect size for each of 5 mosquitoes species. As far, my analysis strategy is:

Using PFT (double arcsine) transformation in order to normalize data (I have lots of 0 and 1 as values)
Running overall model to assess heterogeneity (test for residual heterogeneity significative)
As I have several measures coming from each of the articles included in the meta-analysis, I assessed the necessity of using a three-level model (LRT significative, for "measure" and "article" (measure nested in article)
Using subgroup analysis, with mosquitoes species as moderator
Assessing for residual heterogeneity
Testing some moderators to try to explain residual heterogeneity

When I performed subgroup analysis, I got a test for moderator significative for the variable "Specie". But I then wanted to know which part of variance is explained by this significative variable, and I obtained -0.9% (truncated at 0%) (I used "pseudo R-squared" method suggested by W. Viechtbauer here).

So, my question is: would it be possible/coherent to have a test of moderators significative and no variance explained by this moderator ? How to explain it ?

As I use REML estimation, I can't use LRT to test if the variable is significative (and I would rather not use ML back just to compute LRT).

Thanks in advance if someone can help me,

Best regards,

Alex

If useful, here is a abstract of the code I used:

ies.da <- escalc(xi = data_test[, "n"], 
                 ni = data_test[, "n_tested"],
                 data = data, 
                 measure = "PFT", 
                 add = 0)
  
subganal.specie.mv <- rma.mv(yi, vi, 
                             data = ies.da, 
                             mods = ~factor(Specie), 
                             method = "REML",
                             random = ~1|article/measure)

subganal.no.specie.mv <- rma.mv(yi, vi, 
                                data = ies.da, 
                                method = "REML",
                                random = ~1|article/measure)

pseudo.r.squared <- (sum(subganal.no.specie.mv$sigma2) - sum(subganal.specie.mv$sigma2)) / sum(subganal.no.specie.mv$sigma2)

As result, I get a test of moderator significative:

subganal.specie.mv

Multivariate Meta-Analysis Model (k = 165; method: REML)

Variance Components:

            estim    sqrt  nlvls  fixed     factor 
sigma^2.1  0.0184  0.1358     21     no     article
sigma^2.2  0.0215  0.1466    165     no     article/measure 

Test for Residual Heterogeneity:
QE(df = 161) = 897.9693, p-val < .0001

Test of Moderators (coefficients 2:4):
QM(df = 3) = 12.3578, p-val = 0.0063

Model Results:

                  estimate      se     zval    pval    ci.lb    ci.ub 
intrcpt             0.6172  0.1051   5.8729  <.0001   0.4112   0.8232  *** 
factor(Specie)1    -0.0123  0.1240  -0.0990  0.9211  -0.2554   0.2308      
factor(Specie)2    -0.2110  0.1178  -1.7913  0.0733  -0.4419   0.0199    . 
factor(Specie)3    -0.2299  0.1008  -2.2813  0.0225  -0.4274  -0.0324    *

But my "pseudo R squared" is null:

pseudo.r.squared
[1] -0.009012437

How to interpret significant test of moderators and null percentage of explained variance in meta-analysis using "metafor" package?

0 Answers0