[R-meta] Meta-Analysis and Forest Plot for Multiple Treatments and Outcomes

[Ruth Appel]

Hi Gerta,

Thank you so much for your super helpful and quick reply!
Yes, that is correct, I used the netmeta package as well (I considered it a complement/extension of meta [part of the yet to be established metaverse ;)], but I should have mentioned all packages I was using). The combination of netpairwise() and forest() is very close to what I was looking for – it would only be perfect if I could plot all 8 outcomes in the same plot rather than showing 8 separate plots, and I am not sure whether that’s possible since netpairwise seems to configure the different comparisons as subgroups and I couldn’t see another option to specify that I would like to show effects for several outcomes.

That is an important note regarding potential inconsistency issues with Hedges’ g, I could use Cohen’s d in that case.

Regarding the correlation between outcomes, how strong could it potentially bias the results in your experience? I think the netpairwise() solution is great, so if the bias introduced is not too big, I might use that approach.

Best,
Ruth

Ruth Elisabeth Appel
Ph.D. Candidate in Media Psychology
Stanford University Department of Communication
rappel using stanford.edu<mailto:rappel using stanford.edu>

On Jan 31, 2022, at 10:34 AM, Dr. Gerta Rücker <ruecker using imbi.uni-freiburg.de<mailto:ruecker using imbi.uni-freiburg.de>> wrote:

Hi Ruth,

First of all, if I understand it correctly, what you are aiming at is a network meta-analysis (NMA). Therefore, meta is not the appropriate R package, which would be netmeta (specialized to NMA) or metafor (more general). It seems you have in fact used netmeta, because you write about a netmeta object, is that true? I would see the NMA as the primary analysis and the pairwise meta-analyses as sensitivity analyses. These can be conducted using function netpairwise() in netmeta; for the fixed effect model, also netsplit() should provide the direct pairwise comparisons. Perhaps @Guido Schwarzer sees a convenient way to visualize the results within the same forest plot using forest.netsplit().

I would expect a problem with Hedges' g for three-arm studies because the results within a trial may become inconsistent (this holds for Hedges' g, but not for Cohen's d, as implemented in netmeta).

Note that netmeta accounts for multiple comparisons between groups with a study, however, it does not handle multivariate outcomes. Thus, if you want to account for correlation between outcomes, you need metafor. With respect to metafor, others are more expert than me.

Best,

Gerta

Am 31.01.2022 um 19:02 schrieb Ruth Appel:
Hi all,

I’m currently conducting my first meta-analysis, an internal meta-analysis to summarize the result of 3 similar studies my colleagues and I conducted.

I looked at the documentation of various meta-analysis packages and tutorials, but I am still not fully sure about the best approach.
The experiments I’m analyzing all have a similar structure (2 treatment groups, 1 control group; 8 different outcomes (measuring different constructs)). The raw data has repeated measures, but we look at outcomes at the group level, so I calculated all necessary summary statistics (mean, sd, n).
My goal is to create a forest plot that shows Hedges’ g estimated using an FE model (because the studies were highly similar) for (1) all 3 studies individually and (2) across all studies. Ideally, the final result would be a single forest plot with individual study estimates and across study estimate grouped by outcome.

I managed to create such a plot with the meta package for the 2 treatment groups separately, but I realized that my SEs could be biased in this case because I’m not accounting for the correlations in the variance resulting from the comparison of two treatment groups to the same control group. Similarly, I found a workaround to show all outcomes in 1 forest plot by using subgroups for the different outcomes, but I do not take into consideration that outcomes might be correlated within studies. I also didn’t find a way to show the individual study results in addition to the overall network results in a forest plot of a netmeta object.

I then tried to calculate the correct values using metafor and following the tutorial at https://www.metafor-project.org/doku.php/analyses:gleser2009#multiple-treatment_studies, but it seems like the individual studies are not correctly identified in the output (the ids are all unique instead of matching the study variable I had created).

My questions are: (1) Did I overlook guidance somewhere on how to exactly specify a model like the one above using the metafor, meta (or another R) package, and generate a forest plot for it?
(2) If this is not easily possible, do you think the bias introduced should be sufficiently small such that acknowledging it and presenting separate meta-analyses for each treatment, and a network meta analysis with the overall effects of each treatment (separately for each outcome) in the appendix, is acceptable? (I had very similar estimates across all the approaches described above.)

Best regards, and thank you very much for your guidance,
Ruth

Ruth Elisabeth Appel
Ph.D. Candidate in Media Psychology
Stanford University Department of Communication
rappel using stanford.edu<mailto:rappel using stanford.edu>

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--

Dr. rer. nat. Gerta Rücker, Dipl.-Math.

Guest Scientist
Institute of Medical Biometry and Statistics,
Faculty of Medicine and Medical Center - University of Freiburg

Zinkmattenstr. 6a, D-79108 Freiburg, Germany

Mail:     ruecker using imbi.uni-freiburg.de<mailto:ruecker using imbi.uni-freiburg.de>
Homepage: https://www.uniklinik-freiburg.de/imbi-en/employees.html?imbiuser=ruecker



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