[R-meta] Questions about Omnibus tests

[Rafael Rios]

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> 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(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(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]
> Sent: Tuesday, 30 October, 2018 6:16
> To: Viechtbauer, Wolfgang (SP)
> Cc: Michael Dewey; 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(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
>

	[[alternative HTML version deleted]]