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

Van Meter, Anna @v@nmeter @ending from northwell@edu
Mon Dec 24 14:20:18 CET 2018


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 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

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