[R-sig-ME] Using r for multi-level meta-analysis

[Mike Cheung]

Hi,

The meta3 function in the metaSEM package has implemented the three-level
meta-analysis using the SEM approach. The metafor package has also
implemented it using the multilevel modelling approach.

Regards,
Mike

On Sunday, 15 May 2016, איציק פרדקין <itzikf at outlook.com> wrote:

> Dear R and MLM experts,I'm trying to figure out whether it's possible to
> implement Van den Noortgate (2014) approach for three-level meta-analysis
> in lme4 or nlme. In my data structure I have several outcomes per study,
> and the three levels are: Level 1 - regressing observed effect size on its
> estimated population effect size + residual errorLevel 2- regressing each
> outcome and study estimated population effect size on the study overall
> population effect size + errorLevel 3 - regressing each study overall
> population effect size on the mean effect size of all studies + error
> The special case of meta-analysis doesn't require the estimation of the
> residual error at level 1, because it is estimated by the variance of the
> effect size (e.g. variance of Hedges g), which is given for each outcome
> and study. In a regular meta-analysis model, the inverse of this variance
> is used to weight different studies when combining them to an overall mean
> effect size.
> Van den Noortgate provides a SAS script (using Proc mixed) for this
> purpose. Specifically, he suggested that weighting effects sizes according
> to their respective weight (1/variance of effect size) , and constraining
> the residual error term to 1, which should constrain the residual error of
> each outcome and study to the given variance of this effect size. I attach
> below the SAS code he provided.
>
> I was wondering whether it's possible to do the same by using R MLM
> packages. specifically - I'm stuck with how to constrain the level 1 errors
> to 1.
> Thanks a lot!Isaac.
>
>
> Proc mixed data=D method=reml;         class Study Outcome         model
> ES= /solution ddfm=satterhwaite;         weight W;         random
> intercept/sub=Study;         random intercept/sub=Outcome;         params 1
> 1 1/hold=3run;
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>
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 Mike W.L. Cheung               Phone: (65) 6516-3702
 Department of Psychology       Fax:   (65) 6773-1843
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 http://courses.nus.edu.sg/course/psycwlm/internet/
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