[R-meta] question about effect size estimates using Berkey

[Michael Dewey]

Dear Anna

I am not an authority on multivariate meta-analysis but a similar 
phenomenon is well known for univariate so I suspect that the situation 
is that when you use a moderator you are using all the data-set to 
estimate variances whereas when you subset you are just using that subset.

http://www.metafor-project.org/doku.php/tips:comp_two_independent_estimates

Explains the situation for the univariate case.

Michael

On 23/12/2018 16:40, Van Meter, Anna wrote:
> Hello,
> 
> 
> 
> I have a question about inconsistent results that I am getting when trying to calculate effect sizes using the Berkey method. The data are prevalence rates from different studies and some studies include multiple prevalence rates, which may reflect the same people. For example, one study could report the prevalence for bipolar I and for the full bipolar spectrum, which would include those with bipolar I, plus others who have other subtypes of bipolar disorder. There are three potential prevalence categories � bipolar I, bipolar I & II, all bipolar.
> 
> 
> 
> I have created two binary dummy codes to represent which subtypes are included in each sample � inc2yn (bipolar I & II) and nosyn (all bipolar). There is also a code that says which of the three categories an effect size belongs to (threegroup) and is coded 1 (bipolar I), 2 (bipolar I & II) or 3 (all bipolar).
> 
> 
> 
> Id refers to individual effects sizes, articleno is the study, so Ids are nested within articleno.
> 
> 
> 
> There are 27 effect sizes from 18 studies. There are 13 bipolar I effects, 7 bipolar I & II effects, and 7 all bipolar effects.
> 
> 
> 
> Initially, I ran the following code to get estimates for the three effect sizes:
> 
> resmvberkey0<-rma.mv(yi, berkeyV, data=kidtall1, mods= cbind(inc2yn, nosyn), slab = paste(reference),random = list((~ 1 | Id), ~ 1 | articleno), method="ML")
> 
> 
> 
> Then, to get an estimate for bipolar I & II (for example). I would use the following command:
> 
> predict(resmvberkey0, transf=transf.ilogit, newmods = cbind(1,0))
> 
> 
> 
> Later, when I was making a forest plot, I wanted to get estimates from a model that did not include moderators (it seems that you cannot include moderators in the addpoly command). This led me to use the subset command to get estimates for each effect size separately:
> 
> resmvberkey0bp1_2<-rma.mv(yi, berkeyV, data=kidtall1, subset=threegroup==2, random = list((~ 1 | Id), ~ 1 | articleno), method="ML")
> 
> predict(resmvberkey0bp1_2, transf=transf.ilogit)
> 
> 
> 
> The rates I get by manipulating the moderators to estimate for a single category are different from the rates I get when I use the subset command:
> 
> moderator result for bipolar I = .11%
> 
> subset result for bipolar I = .15%
> 
> 
> 
> moderator result for bipolar I & II = .18%
> 
> subset result for bipolar I & II is .17%
> 
> 
> 
> moderator result for all bipolar is 1.51%
> 
> subset result for all bipolar is 3.56%
> 
> 
> 
> Any thoughts about why these results would be so different � and why � would be greatly appreciated.
> 
> 
> 
> Thank you,
> 
> Anna
> 
> 
> Anna Van Meter, PhD
> 
> Assistant Professor, The Feinstein Institute for Medical Research
> 
> Adjunct Assistant Professor, Ferkauf Graduate School of Psychology, Yeshiva University
> 
> The Zucker Hillside Hospital, Division of Psychiatry Research
> 
> 75-59 263rd Street
> 
> Glen Oaks, NY 11004
> 
> 718.470.5813
> 
> 
> 
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-- 
Michael
http://www.dewey.myzen.co.uk/home.html