[R-meta] Different outputs by comparing random-effects model with a MLMA without intercept

[Viechtbauer, Wolfgang (SP)]

-----Original Message-----
From: Michael Dewey [mailto:lists using dewey.myzen.co.uk] 
Sent: Thursday, 07 March, 2019 18:06
To: Rafael Rios; Viechtbauer, Wolfgang (SP); r-sig-meta-analysis using r-project.org
Subject: Re: [R-meta] Different outputs by comparing random-effects model with a MLMA without intercept

Dear Rafael

I think this may be related to the issue outlined by Wolfgang in this 
section of the web-site

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

Michael

On 07/03/2019 16:46, Rafael Rios wrote:
> Dear Wolfgang and All,
> 
> I am conducting a meta-analysis to evaluate potential bias of a fixed
> predictor with two subgroups (predictor: yes and no). Because I found a
> bias, I removed the values of subgroup "yes" and performed a random-effects
> model. However, when I compared the output of the first model without
> intercept with the output of the random effects model, I obtained different
> results, especially in the estimation of confidence intervals. I was
> expecting to found similar results because the model without intercept
> tests if the average outcome differs from zero. Can you explain in which
> case this can happen? Every help is welcome.
> 
> 
> model1=rma.mv(yi, vi, mods=~predictor-1, random = list (~1|effectsizeID,
> ~1|studyID, ~1|speciesID), R=list(speciesID=phylogenetic_correlation),
> data=h)
> 
> #Multivariate Meta-Analysis Model (k = 1850; method: REML)
> #
> #Variance Components:
> #                    estim     sqrt      nlvls  fixed        factor     R
> #sigma^2.1  0.0145  0.1204   1850    no  effectsizeID   no
> #sigma^2.2  0.0195  0.1397    468     no       studyID     no
> #sigma^2.3  0.2386  0.4885    348     no     speciesID   yes
> #
> #Test for Residual Heterogeneity:
> #QE(df = 1848) = 10797.5993, p-val < .0001
> #
> #Test of Moderators (coefficients 1:2):
> #QM(df = 2) = 17.6736, p-val = 0.0001
> #
> *#Model Results:*
> *#                          estimate      se        zval       pval
> ci.lb <http://ci.lb>   ci.ub *
> *#potential_sceno     0.2843  0.1659  1.7141  0.0865  -0.0408  0.6095  *.
> #potential_sceyes    0.3741  0.1668  2.2421  0.0250   0.0471  0.7011  *
> #---
> #Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> 
> 
> model2=rma.mv(zf, vzf, random = list (~1|effectsizeID, ~1|studyID,
> ~1|speciesID), R=list(speciesID=phylogenetic_correlation),
> data=subset(h,potential_sce=="no"))
> 
> #Multivariate Meta-Analysis Model (k = 1072; method: REML)
> #
> #Variance Components:
> #            estim    sqrt  nlvls  fixed        factor    R
> #sigma^2.1  0.0140  0.1184   1072     no  effectsizeID   no
> #sigma^2.2  0.0394  0.1986    264     no       studyID   no
> #sigma^2.3  0.0377  0.1943    240     no     speciesID  yes
> #
> #Test for Heterogeneity:
> #Q(df = 1071) = 4834.5911, p-val < .0001
> #
> *#Model Results:*
> *#estimate      se    zval    pval   ci.lb <http://ci.lb>   ci.ub *
> *#  0.2989  0.0720  4.1494  <.0001  0.1577  0.4401  *** *
> #---
> #Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
> 
> 
> I used another data set to conduct the same approach and obtained similar
> results:
> 
> dat <- dat.bangertdrowns2004
> rbind(head(dat, 10), tail(dat, 10))
> dat <- dat[!apply(dat[,c("length", "wic", "feedback", "info", "pers",
> "imag", "meta")], 1, anyNA),]
> 
> head(dat)
> 
> random.model=rma.mv(yi, vi, random=list(~1|id, ~1|author), structure="UN",
> data=subset(dat, subject=="Math"))
> 
> random.model
> 
> *#Math*
> *#Model Results:*
> *#  estimate      se    zval         pval   ci.lb <http://ci.lb>   ci.ub *
> *#    0.2106  0.0705  2.9899  0.0028  0.0726  0.3487  ***
> 
> mixed.model=rma.mv(yi, vi, mods=~subject-1, random=list(~1|id, ~1|author),
> structure="UN", data=dat)
> 
> anova(mixed.model,btt=2)
> 
> *#Math*
> *#  estimate      se       zval    pval       ci.lb <http://ci.lb>   ci.ub*
> *#    0.2100  0.0697   3.0122  0.0026   0.0734  0.3467*
> 
> Best wishes,
> 
> Rafael.
> __________________________________________________________
> 
> Dr. Rafael Rios Moura
> *scientia amabilis*
> 
> Behavioral Ecologist, Ph.D.
> Postdoctoral Researcher
> Universidade Estadual de Campinas (UNICAMP)
> Campinas, São Paulo, Brazil
> 
> ORCID: http://orcid.org/0000-0002-7911-4734
> Currículo Lattes: http://lattes.cnpq.br/4264357546465157
> Research Gate: https://www.researchgate.net/profile/Rafael_Rios_Moura2