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

[Viechtbauer Wolfgang (STAT)]

You may also find this of interest: http://www.metafor-project.org/doku.php/tips:rma_vs_lm_and_lme

Best,
Wolfgang

-- 
Wolfgang Viechtbauer, Ph.D., Statistician | Department of Psychiatry and    
Neuropsychology | Maastricht University | P.O. Box 616 (VIJV1) | 6200 MD    
Maastricht, The Netherlands | +31 (43) 388-4170 | http://www.wvbauer.com    

> -----Original Message-----
> From: R-sig-mixed-models [mailto:r-sig-mixed-models-bounces at r-
> project.org] On Behalf Of Mike Cheung
> Sent: Sunday, May 15, 2016 14:52
> To: איציק פרדקין
> Cc: r-sig-mixed-models at r-project.org
> Subject: Re: [R-sig-ME] Using r for multi-level meta-analysis
> 
> 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;