[R] Mean effect size in meta-analysis using Metafor

Carlijn . wibbeltjec at hotmail.com
Mon Dec 21 14:34:50 CET 2015

```Dear Michael,

Thanks for your reaction. The estimates in the example in the link are not exactly identical, but almost. However, in my case, there is a substantial difference between the estimates of the overall effect.

For example: I want to estimate the effect size of different mental health disorders. I start with estimating the effect size of substance use disorder (SUD), using a three-level random effects model.

In the first approach I have fitted an intercept only model with a subset of the data including only data on SUD. The overall effect size of SUD is d = 0.185 (SE = 0.085), p = .033.

Using the second approach, I have included a categorical moderator 'disorder' (SUD, DBD, ADHD). The reference group is SUD and I have added the predictors DBD (i.e., DBD is coded with '1', and SUD and ADHD with '0') and ADHD (i.e., ADHD is coded with '1' and SUD and DBD with '0'). The mean effect size of SUD (intercept) is d = 0.300 (SE = 0.104), p = .005.

To conclude, there is a substantial difference between the estimated effect sizes (d = 0.185 versus d = 0.300).

Approach 1:
> summary(sud, digits=3)
Multivariate Meta-Analysis Model (k = 49; method: REML)
logLik  Deviance       AIC       BIC      AICc
-11.808    23.616    29.616    35.230    30.161

Variance Components:
estim   sqrt  nlvls  fixed  factor
sigma^2.1  0.042  0.206     49     no       y
sigma^2.2  0.050  0.223     13     no      ID

Test for Heterogeneity:
Q(df = 48) = 409.874, p-val < .001

Model Results:

estimate    se         tval       pval       ci.lb      ci.ub
0.185      0.085    2.189    0.033    0.015    0.356        *

Approach 2:
> summary(external, digits=3)
Multivariate Meta-Analysis Model (k = 123; method: REML)
logLik  Deviance       AIC       BIC      AICc
-58.470   116.940   126.940   140.878   127.467

Variance Components:
estim   sqrt  nlvls  fixed  factor
sigma^2.1  0.065  0.256    123     no     y
sigma^2.2  0.113  0.336     17     no      ID

Test for Residual Heterogeneity:
QE(df = 120) = 846.602, p-val < .001

Test of Moderators (coefficient(s) 2,3):
QM(df = 2) = 0.956, p-val = 0.387

Model Results:

estimate     se       tval       pval      ci.lb  ci.ub
intrcpt      0.300       0.104     2.874    0.005    0.093  0.506  **
DBD          0.114       0.084     1.354     0.178   -0.053  0.281
ADHD        0.087       0.104     0.836    0.405   -0.119  0.293

> Subject: Re: [R] Mean effect size in meta-analysis using Metafor
> To: wibbeltjec op hotmail.com; r-help op r-project.org
> From: lists op dewey.myzen.co.uk
> Date: Sat, 12 Dec 2015 16:12:58 +0000
>
> Dear Carlijn
>
> I wonder whether
>
> http://www.metafor-project.org/doku.php/tips:comp_two_independent_estimates
>
> procedure we might know for sure.
>
>
> On 12/12/2015 15:35, Carlijn . wrote:
> >
> >
> > Hi all,
> >
> >
> >
> > I have a question about doing a meta-analysis, in particular a three-level
> > meta-analysis using Metafor.
> >
> > I have estimated the mean overall effect size of males by using two different
> > ways:
> >
> > 1. moderator analysis (male = 0, female = 1) using the whole data set
> >
> > 2. intercept-only model with a subset of the data (only males)
> >
> >
> >
> > The mean effect size estimated by using the categorical moderator analysis
> > (1) differs considerably from the overall mean effect size estimated in an
> > intercept-only model using a subset of the data (2).
> >
> >
> >
> > Can someone explain this? Which method gives a better estimation of the
> > effect?
> >
> >
> >
> > Thank you in advance!
> >
> >
> > 	[[alternative HTML version deleted]]
> >
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