[R-meta] Results interpretation publication bias

Michael Dewey ||@t@ @end|ng |rom dewey@myzen@co@uk
Mon Apr 3 14:55:06 CEST 2023

Dear Wilma

Comments in-line

On 03/04/2023 10:14, Wilma Charlott Theilig via R-sig-meta-analysis wrote:
> Hey all,
> I am currently conducting my first meta-analysis using a random effects model. I wanted to investigate whether there is publication bias in my studies using different small-study effects methods. Egger's test and rank correlation test indicate funnel plot asymmetry. I then used the trim and fill method twice.

If you are interested in publication bias, as opposed to small-study 
effects more generally, then
suggests that a selection model would be preferable to trim and fill.

> Once with all studies and since I had a very high I2 value (98.4%) and then again but with excluded outliers (here I used the find.outlier function from dmetar to identify 39 of 79 studies as outliers) as a sensitivity analysis.

A method which identifies the majority of your data as outliers is not 
detecting outliers in the normal meaning of the word. Is it telling you 
you have a mixture distribution of effect sizes? When you look at them 
do you see any common feature about the primary studies? That is where I 
would look first to try to identify possible moderators.

> Both values are higher (r= .57 and r= .38 without outliers) than than the original overall pooled effect size (r= .36). So my interpretation would be: there seems to be "missing" mainly large effect sizes with small standard errors (i.e. large studies reporting large effect sizes, at least according to the contour enhanced funnel plot).

Although this list deprecates attachments actually seeing the funnel 
plot might help here. I think the list might accept pdf attachments.


  But maybe it is also due to high heterogeneity? How can I interpret 
this result? I would greatly appreciate an answer a
>   nd some help.
> Best regards
> Wilma
> 	[[alternative HTML version deleted]]
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