-----Original Message-----
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-project.org]
On Behalf Of Dylan Johnson
Sent: Wednesday, 09 December, 2020 19:37
To: James Pustejovsky; Tobias Saueressig
Cc: R meta
Subject: Re: [R-meta] Egger's test with multilevel meta analysis
I have tried the following:
egger_multi <- rma.mv(HEDGE_G, HEDGE_VAR, random = ~ 1 | COHORT_ID,
EFFECT_ID, mods = ~ STD_ERR, data = dataset)
coeftest(egger_multi, vcov = "CR2")
When I run the coeftest I receive the error:
Error in diag(se) : invalid 'nrow' value (too large or NA)
In addition: Warning message:
In diag(se) : NAs introduced by coercion
Dylan Johnson, MSc
MA Student, School and Clinical Child Psychology
Department of Applied Psychology and Human Development
University of Toronto
252 Bloor Street West
Toronto, ON M5S 1V6
From: James Pustejovsky<mailto:jepusto at gmail.com>
Sent: December 9, 2020 1:20 PM
To: Tobias Saueressig<mailto:t.saueressig at gmx.de>
Cc: Dylan Johnson<mailto:dylanr.johnson at mail.utoronto.ca>; R meta<mailto:r-
sig-meta-analysis at r-project.org>
Subject: Re: [R-meta] Egger's test with multilevel meta analysis
We have a paper (forthcoming in Psych Methods) evaluating a similar method
for adapting Egger's test to the multilevel context, using RVE:
* Rodgers, M. A., & Pustejovsky, J. E. (In Press). Evaluating Meta-Analytic
Methods to Detect Selective Reporting in the Presence of Dependent Effect
Sizes. Psychological Methods, forthcoming.
https://doi.org/10.31222/osf.io/vqp8u
There is also a related paper by Fernandez-Castilla and colleagues:
* Fern?ndez-Castilla, B., Declercq, L., Jamshidi, L., Beretvas, S. N.,
Onghena, P., & Van den Noortgate, W. (2019). Detecting selection bias in
meta-analyses with multipleoutcomes: A simulation study. The Journal of
Experimental Education, 1?20.
These tests can be implemented in rma.mv<http://rma.mv>() simply by
including the standard error of the effect size (or a related measure of
precision, such as the sample size) as a moderator. Say that data includes a
variable called sei for the standard error of each effect size:
egger_multi <- rma.mv<http://rma.mv>(yi = yi, V = sei^2, random = ~ 1 |
studyID, effectID, mods = ~ sei, data = dat)
Then apply cluster-robust standard errors for the RVE-based test:
coef_test(egger_multi, vcov = "CR2")
Further details available in our paper, and example code in our
supplementary materials.
James