[R-meta] multilevel models and bias assessment

Lukasz Stasielowicz |uk@@z@@t@@|e|ow|cz @end|ng |rom un|-o@n@brueck@de
Fri Dec 31 14:10:56 CET 2021

Dear Catia,

one could conduct a modified Egger's regression test by accounting for 
the dependency (e.g. StudyID/EffectID and respective variance-covariance 
matrix) and using a precision estimate as moderator variable, e.g.

model <- rma.mv(Effects, Vmatrix, mods = ~ Precision,random = ~ 1 | 
StudyID/EffectID, data = data)

Please note that the choice of the precision estimate depends on the 
effect size (e.g., r, d): "A complication with Egger’s regression is 
that for certain effect size metrics, the standard error is naturally
correlated with the effect size estimate even in the ab-
sence of selective reporting or other sources of asym-
metry. Different variants of Egger’s regression have
been developed to reduce the correlation by using alter-
native measures of precision, specifically for log odds
ratios (Macaskill, Walter, & Irwig, 2001; Moreno et
al., 2009; Peters et al., 2006), raw proportions (Hunter
et al., 2014), hazard ratios (Debray, Moons, & Riley,
2018), and standardized mean differences (Pustejovsky
& Rodgers, 2019)."

If you are using correlation coefficients then see this reply from Wolfgang:

For recommendations about other effect sizes you can use the references 
in the cited article:
Rodgers, M. A., & Pustejovsky, J. E. (2021). Evaluating meta-analytic 
methods to detect selective reporting in the presence of dependent 
effect sizes. Psychological Methods, 26(2), 141–160. 

Best wishes,
Lukasz Stasielowicz
Osnabrück University
Institute for Psychology
Research methods, psychological assessment, and evaluation
Seminarstraße 20
49074 Osnabrück (Germany)

Am 28.12.2021 um 12:00 schrieb r-sig-meta-analysis-request using r-project.org:
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> Subject: [R-meta] multilevel models and bias assessment
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> Dear all,
> I hope you are well.
> Given that we should not conduct Egger's regression on effect sizes with
> dependency, would it be more adequate to aggregate all effect sizes coming
> from the same study or is it OK to just combine effect sizes when they come
> from the same participants? I know the latter ignores the nested nature of
> the data, so I just wanted to check whether it is adequate to do so just
> for bias assessment.
> Best wishes,
> Catia

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