[R-meta] Assessing publication bias from multilevel modelling
Célia Sofia Moreira
celiasofiamoreira at gmail.com
Wed Feb 14 18:56:04 CET 2018
Dear Michael and James,
Thank you very much for your nice recommendations. Meanwhile, I don't know
if it is just by chance, but the funnel plots of
mm<-rma.mv(y ~ 1, V=Vlist, random = ~ 1 |Study, struct = "UN", data =
base); #Vlist <- impute_covariance_matrix(vi = base$v, cluster =
base$Study, r = .5)
m<-rma(y ~ 1, v, data = base);
are precisely the same (although the models' summaries are different)! I
was not expecting this result.
Does this make sense to you? Maybe "funnel(mm)" is not taking into account
the random part of the model, I don't know... Can you please comment about
2018-02-14 17:36 GMT+00:00 James Pustejovsky <jepusto at gmail.com>:
> I think regtest() is appropriate only for effect sizes that are
> statistically independent. It would not be appropriate for Celiia's case,
> where there is dependence among multiple effects from a given study.
> Egger's regression test can be conducted simply by using the standard
> errors of the effect size estimates (i.e., the "sei") as a predictor in a
> meta-regression. Using cluster-robust variance estimation with this
> meta-regression will take care of the dependence issue. A significant
> coefficient on the standard error predictor would indicate funnel plot
> asymmetry. (Although as always, a lack of statistically significance does
> not *prove* that the funnel plot is symmetric.) When I've used this
> technique, I fit the meta-regression without any random effects in the
> model so that larger studies are given relatively more weight for
> estimating the meta-regression coefficients.
> The Henmi-Copas model is limited to univariate models. I don't know of a
> way to generalize it for multi-variate meta-analysis.
> On Wed, Feb 14, 2018 at 11:21 AM, Michael Dewey <lists at dewey.myzen.co.uk>
>> Dear Célia
>> As far as I can see regtest can accept the yi, vi or sei parameters so
>> you should be OK there. I do not think the HC method is going to be easy to
>> do if it is even possible.
>> On 14/02/2018 16:14, Célia Sofia Moreira wrote:
>>> I am studying a pretest-posttest controlled design and I'm using metafor
>>> and clubSandwich packages. In some papers, I collected more than one
>>> size (from the same study sample). For this reason, I did multilevel
>>> modelling using rma.mv function, with
>>> random = ~ 1 |Study, struct = "UN".
>>> I would like to perform a publication bias analysis, using funnel plots,
>>> tests for funnel plots asymmetry, and Hemni and copas (HC) model.
>>> since I am using rma.mv function, I can only depict (multilevel) funnel
>>> plots; unfortunately, tests for funnel plots asymmetry and HC model are
>>> available for rma.mv....
>>> Do you know any alternative way to test the asymmetry of my "multilevel"
>>> funnel plots, as well as to compare with the HC modelling?
>>> If not, can anybody please suggest other methods to assess publication
>>> in this multilevel case?
>>> Thank you very much,
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