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 at stanford.edu <mailto:rappel at stanford.edu>
On Jan 31, 2022, at 10:34 AM, Dr. Gerta R?cker
<ruecker at imbi.uni-freiburg.de <mailto:ruecker at 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
<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 at stanford.edu <mailto:rappel at stanford.edu>