[R-meta] Questions about Omnibus tests

Rafael Rios bior@f@elrm @ending from gm@il@com
Sun Nov 4 15:08:24 CET 2018


Dear Michael,

Thanks for the advise and the reference. I did not propose to permanently
delete outliers from the analysis. This approach is only useful to evaluate
if I can obtain the same conclusions with and without the outliers, but
both results should be showed in the paper. Alternatively, I don't know if
I can use a sensitivity analysis for rma.mv function to test the
consistency of the results. I am concerned about to draw biased conclusions
on differences or similarities in average effect size between subgroups.

Best wishes,

Rafael.
__________________________________________________________

Dr. Rafael Rios Moura
*scientia amabilis*

Behavioral Ecologist, PhD
Postdoctoral Researcher
Universidade Estadual de Campinas (UNICAMP)
Campinas, São Paulo, Brazil

Currículo Lattes: http://lattes.cnpq.br/4264357546465157
ORCID: http://orcid.org/0000-0002-7911-4734
Research Gate: https://www.researchgate.net/profile/Rafael_Rios_Moura2




<http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4244908A8>


Em dom, 4 de nov de 2018 às 11:43, Michael Dewey <lists using dewey.myzen.co.uk>
escreveu:

> Dear Rafael
>
> I will let Wolfgang respond about the details of models but as far as
> heterogeneity is concerned.
>
> Eliminating outliers without concrete reasons may be removing the most
> interesting observations.
>
> If the primary studies are well estimated then heterogeneity is bound to
> be large. See for example Rücker, G, Schwarzer, G, Carpenter, J R and
>   Schumacher, M, Undue reliance on I^2 in assessing heterogeneity may
> mislead BMC Medical Research Methodology 2008 (8) 79
>
> It is really the pattern of heterogeneity which you need to examine in
> your funnel plot and then try to explain it.
>
> Michael
>
> On 03/11/2018 23:31, Rafael Rios wrote:
> > Dear Wolfgang,
> >
> > Could you please help me again with new questions?
> >
> > Should I build model1 rather than model2 to control for the dependency
> > among studyID and effectsizeID?
> >
> > model1=rma.mv <http://rma.mv>(zf, vzf, mods=~mate_choice,
> > random=list(~1|studyID/effectsizeID, ~1|species1), data = h_mc)
> > model2=rma.mv <http://rma.mv>(zf, vzf, mods=~mate_choice,
> > random=list(~1|effectsizeID, ~1|studyID, ~1|species1), data = h_mc)
> >
> > I used your script to calculate I² and found a high heterogeneity in my
> > model (86.63%).
> > #I²:
> http://www.metafor-project.org/doku.php/tips:i2_multilevel_multivariate
> > W <- diag(1/h_mc$vzf)
> > X <- model.matrix(model1)
> > P <- W - W %*% X %*% solve(t(X) %*% W %*% X) %*% t(X) %*% W
> > 100 * sum(meta$sigma2) / (sum(meta$sigma2) +
> (meta$k-meta$p)/sum(diag(P)))
> >
> > Do you have suggestions on how to handle with high heterogeneity among
> > effect sizes? How may I conduct sensitivity tests in a multilevel
> > meta-analysis using metafor? I identified (using a funnel plot) and
> > removed outliers to reduce the heterogeneity and redo the model. Is this
> > approach suitable to evaluate potential bias in results? Or are there
> > better alternatives?
> >
> > Best wishes,
> >
> > Rafael.
> > __________________________________________________________
> >
> > Dr. Rafael Rios Moura
> > /scientia amabilis/
> >
> > Behavioral Ecologist, PhD
> > Postdoctoral Researcher
> > Universidade Estadual de Campinas (UNICAMP)
> > Campinas, São Paulo, Brazil
> >
> > Currículo Lattes: http://lattes.cnpq.br/4264357546465157
> > ORCID: http://orcid.org/0000-0002-7911-4734
> > Research Gate: https://www.researchgate.net/profile/Rafael_Rios_Moura2
> >
> >
> >
> >
> > <http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4244908A8>
> >
> >
> > Em ter, 30 de out de 2018 às 16:01, Rafael Rios <biorafaelrm using gmail.com
> > <mailto:biorafaelrm using gmail.com>> escreveu:
> >
> >     Dear Wolfgang,
> >
> >     Thank you for the amazing clarifications! I think I finally have a
> >     better picture about the meta-analytic procedures.
> >
> >     Best wishes,
> >
> >     Rafael.
> >     __________________________________________________________
> >
> >     Dr. Rafael Rios Moura
> >     /scientia amabilis/
> >
> >     Behavioral Ecologist, PhD
> >     Postdoctoral Researcher
> >     Universidade Estadual de Campinas (UNICAMP)
> >     Campinas, São Paulo, Brazil
> >
> >     Currículo Lattes: http://lattes.cnpq.br/4264357546465157
> >     ORCID: http://orcid.org/0000-0002-7911-4734
> >     Research Gate:
> https://www.researchgate.net/profile/Rafael_Rios_Moura2
> >
> >
> >
> >
> >     <
> http://buscatextual.cnpq.br/buscatextual/visualizacv.do?id=K4244908A8>
> >
> >
> >     Em ter, 30 de out de 2018 às 15:28, Viechtbauer, Wolfgang (SP)
> >     <wolfgang.viechtbauer using maastrichtuniversity.nl
> >     <mailto:wolfgang.viechtbauer using maastrichtuniversity.nl>> escreveu:
> >
> >         Dear Rafael,
> >
> >         1. "Does the QM-test, with an intercept in the model, evaluates
> >         if the average true outcomes of subgroups differ from the
> >         reference level or from 0?"
> >
> >          >From the reference level.
> >
> >         "Why are the graph results so different from the QM-test with an
> >         intercept in the model?"
> >
> >         Your graph is not correct. It should be:
> >
> >         preds <- predict(meta, newmods=rbind(c(0,0), c(1,0), c(0,1)))
> >         forest(preds$pred, sei=preds$se, slab=c("female", "male",
> "mutual"))
> >
> >         The differences between the three levels are small.
> >
> >         "Should I evaluate results using anova(meta,btt=1:3)?"
> >
> >         anova(meta,btt=1:3) tests if all 3 groups have a zero effect.
> >         That does not test for differences between groups.
> >
> >         "Was the argument linfct=rbind(c(0,0,1)) used to compare the
> >         subgroups of female choice (reference level) and male choice?"
> >
> >         No, this compares 'mutual' with 'female'.
> >
> >         "What am I evaluating by using summary(glht(meta,
> >         linfct=rbind(female=c(1,0,0), male=c(0,1,0))), test=Chisqtest())"
> >
> >         You are evaluating whether the intercept (and hence the effect
> >         for 'female') is 0 and whether there is a difference between
> >         'male' and 'female'.
> >
> >         2. "What is the best approach to measure heterogeneity in a
> >         multilevel meta-analysis?"
> >
> >         I don't know what is best. The link you posted provides some
> >         possibilities for computing I^2-like measures for
> >         multilevel/multivariate models.
> >
> >         3. "I used the standard deviation to weight the effect sizes,
> >         according to Zaykin (2011). Is variance a better measure of
> >         weight than se in a multilevel meta-analysis?"
> >
> >         As mentioned by Michael, this article is irrelevant.
> >
> >         4. "An alternative could be to include this potential_sce as a
> >         fixed variable."
> >
> >         Sure.
> >
> >         "Is this model more appropriate?: meta=rma.mv
> >         <http://rma.mv>(zf, sezf, mods=~mate_choice+potential_sce,
> >         random = list (~1|effectsizeID, ~1|studyID, ~1|species1), data =
> >         h_mc)"
> >
> >         You should pass the variances to the function:
> >
> >         meta=rma.mv <http://rma.mv>(zf, vzf,
> >         mods=~mate_choice+potential_sce, random = list (~1|effectsizeID,
> >         ~1|studyID, ~1|species1), data = h_mc)
> >
> >         Best,
> >         Wolfgang
> >
> >         -----Original Message-----
> >         From: Rafael Rios [mailto:biorafaelrm using gmail.com
> >         <mailto:biorafaelrm using gmail.com>]
> >         Sent: Tuesday, 30 October, 2018 6:16
> >         To: Viechtbauer, Wolfgang (SP)
> >         Cc: Michael Dewey; r-sig-meta-analysis using r-project.org
> >         <mailto:r-sig-meta-analysis using r-project.org>
> >         Subject: Re: [R-meta] Questions about Omnibus tests
> >
> >         Dear Wolfgang,
> >
> >         Thank you for the very helpful advices! I will be grateful if
> >         you could help me again with my new questions. I organized them
> >         in the topics bellow.
> >
> >         1. Does the QM-test, with an intercept in the model, evaluates
> >         if the average true outcomes of subgroups differ from the
> >         reference level or from 0? I found a p>0.05, probably meaning
> >         that there is no difference among subgroups. However, if you
> >         analyze the graph, there a higher effect size for the subgroup
> >         of female choice compared to others. So, I am not sure about the
> >         best approach to evaluate differences among outcomes. Why are
> >         the graph results so different from the QM-test with an
> >         intercept in the model? Should I evaluate results using
> >         anova(meta,btt=1:3)?
> >
> >         You also suggested that the script for pairwise comparisons was
> >         wrong. According to the link that you provided, it can also be
> >         drawn as summary(glht(meta, linfct=rbind(c(0,0,1), c(0,1,0),
> >         c(0,-1,1))), test=adjusted("none")). Was the argument
> >         linfct=rbind(c(0,0,1)) used to compare the subgroups of female
> >         choice (reference level) and male choice? What am I evaluating
> >         by using summary(glht(meta, linfct=rbind(female=c(1,0,0),
> >         male=c(0,1,0))), test=Chisqtest())?
> >
> >         2. Thank you for the correction of I² formula. What is the best
> >         approach to measure heterogeneity in a multilevel meta-analysis?
> >         Maybe, this one:
> >
> http://www.metafor-project.org/doku.php/tips:i2_multilevel_multivariate
> >
> >         3. I used the standard deviation to weight the effect sizes,
> >         according to Zaykin (2011). Is variance a better measure of
> >         weight than se in a multilevel meta-analysis? Reference: D. V.
> >         Zaykin, Optimally weighted Z-test is a powerful method for
> >         combining probabilities in meta-analysis. J. Evol. Biol. 24,
> >         1836–1841 (2011).
> >
> >         4. Finally, I agree with the exclusion of potential_sce as a
> >         random variable. However, I need to control for this variable.
> >         An alternative could be to include this potential_sce as a fixed
> >         variable. Is this model more appropriate?: meta=rma.mv
> >         <http://rma.mv>(zf, sezf, mods=~mate_choice+potential_sce,
> >         random = list (~1|effectsizeID, ~1|studyID, ~1|species1), data =
> >         h_mc).
> >
> >         Thank you again for the help.
> >
> >         Best wishes,
> >
> >         Rafael.
> >         __________________________________________________________
> >
> >         Dr. Rafael Rios Moura
> >         scientia amabilis
> >
> >         Behavioral Ecologist, PhD
> >         Postdoctoral Researcher
> >         Universidade Estadual de Campinas (UNICAMP)
> >         Campinas, São Paulo, Brazil
> >
> >         Currículo Lattes: http://lattes.cnpq.br/4264357546465157
> >         ORCID: http://orcid.org/0000-0002-7911-4734
> >         Research Gate:
> >         https://www.researchgate.net/profile/Rafael_Rios_Moura2
> >
>
> --
> Michael
> http://www.dewey.myzen.co.uk/home.html
>

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