[R-meta] Within-study variance in a multilevel meta-analytic model

[Tharaka S. Priyadarshana]

Dear all,



In my meta-analysis, I have multiple effect sizes coming from the same
study, and these effects are nested with the studies.

So, to account for both between-study and within-study
variance/heterogeneity, I gave identifiers for each study (StudyID) and
each effect (EffectSizeID), and included them into the model as random
effects, i.e., "random = ~1 | (StudyID / EffectSizeID)".



For this, I received a comment from one of the reviewers as follows,

"It took me a long time to figure out why you are using EffectSizeID as a
random effect, especially since this observation-level random effect
approach is mainly used to control for overdispersion in Poisson
regressions. Based on the response to reviewers, I think you are using it
to account for heteroscedasticity? Focusing on various data sources and
within-study variability is confusing because each effect size presumable
comes from just one land cover data source. Framing this as controlling for
heteroscedasticity would help a lot. If that is not your purpose, please
edit this paragraph to more clearly explain your approach."


I am a bit unsure how should I respond to this reviewer's comment. If I
understand correctly, once we define the between and within-study
variance/heterogeneity
with random variables, we can get the true effects for each observed effect
(i.e. each row of the dataset), which then can be used to measure the
average true effect.


Could someone please let me know whether my interpretation is correct here?
or am I missing something??


Thank you for your help.


With best wishes,

Tharaka

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