<div dir="ltr"><div>Hi Greta (all),<br><br></div><div>Thanks for suggesting netmeta, it sound like an interesting alternative. However, I am wondering if the studies I am dealing with (e.g. as the one described in my previous email) are comparable to 'multiple-arms studies'
that netmeta uses. Note that different altitudinal levels that can be reported by multiple studies are not of different "nature" just because measured to different altitude (m), plus we are interested in the difference in altitude between each comparison. So, it is not like you have a control, then two treatments of "Metformin" and "Acarbose" (two different medicines, I assume?) as in 'Willms1999' of Senn2013 data of netmeta examples... Don't know if I am explaining here. That's why I proposed (and ask for confermation) of using three-level meta-analysis via <a href="http://rma.mv">rma.mv</a> in metafor.<br><br></div><div>Thanks and with my best,<br></div><div>Gabriele<br></div><div><br><br><br><br></div></div><div class="gmail_extra"><br><div class="gmail_quote">On 10 April 2018 at 12:36, Gerta Ruecker <span dir="ltr"><<a href="mailto:ruecker@imbi.uni-freiburg.de" target="_blank">ruecker@imbi.uni-freiburg.de</a>></span> wrote:<br><blockquote class="gmail_quote" style="margin:0 0 0 .8ex;border-left:1px #ccc solid;padding-left:1ex">
<div text="#000000" bgcolor="#FFFFFF">
<p>Dear Gabriele,</p>
<p>This looks like a network meta-analysis (multiple treatment
comparison) as it is popular in medicine when comparing multiple
treatments for the same condition in a meta-analysis. You may try
the R package netmeta
<a class="m_-2443465987254378410moz-txt-link-freetext" href="https://cran.r-project.org/web/packages/netmeta/" target="_blank">https://cran.r-project.org/<wbr>web/packages/netmeta/</a> that is designed
to this aim.</p>
<p>Note that what you call "response ratios" are not response ratios
in the sense of an effect measure for a binary outcome, since your
outcome is not binary. I would rather call this ratios of means.
In netmeta, you can choose this effect measure by taking as
summary measure (argument sm) sm = "ROM". The logarithm is then
applied automatically.<br>
</p>
<p>Best,</p>
<p>Gerta<br>
</p><div><div class="h5">
<br>
<div class="m_-2443465987254378410moz-cite-prefix">Am 08.04.2018 um 18:12 schrieb Gabriele
Midolo:<br>
</div>
</div></div><blockquote type="cite"><div><div class="h5">
<div dir="ltr">Dear all,<br>
<br>
I have a question that is more methodological but somehow
related to metafor. <br>
I want to conduct an (ecological) meta-analysis on specific leaf
area (SLA) response to increased altitdue (i.e. elevation) in
mountain ecosystems. Primary studies selected report the mean (+
SE and sample size) of SLA sampled at different altitudinal
levels. The picture attached is an example of how row primary
data are normally reported in the articles (modified, from Seguí
et al 2018, fig.1c [<a href="https://doi.org/10.1007/s00035-017-0195-9%5D" target="_blank">https://doi.org/10.1007/<wbr>s00035-017-0195-9]</a>).
<br>
The A, B and C (in red) values represents the mean values of SLA
calculated at 1900, 2200 and 2350 m above the sea level (i.e.
altitude) that should, in my opinion, be suitable for
calculating log-transformed response ratios (RR) indicating how
much SLA increases/decreases compared to a population of plants
sampled to a lower altitiude. Thus, given the design of such
studies, I propose that multiple RR (yi) must be calulcated
within each study as follows:<br>
<br>
yi1= ln(B/A)<br>
yi2=ln(C/A)<br>
yi3=ln(C/B)<br>
... <br>
if a D value would have been reported by the authors, sampled to
a higher altitdue than 2350 m, then I woul also calculate
yi4=ln(D/A), yi5=ln(D/B), yi6=ln(D/C) for this study.<br>
<br>
This approach make sense to me because there is no "proper"
control and treatment and you are not just interested to
estimate SLA changes by comparing mean values reported at higher
altitudes with only the one sampled at the lowest altitudinal
level (yi1,yi2), but also between higer altitudinal levels
(yi3). This is also supposed to allow to look in meta-regession
how the altitudinal shift (so, the difference in altitudes e.g.
300m for yi1) affect the effect size responses. So - and here
finally comes my question - with <a href="http://rma.mv" target="_blank">rma.mv</a> I should be able to safely
account for non-independence by fitting a model with the "random
=~1|Experiment/ID" structure (?). Is this type of data suitable
for three-level mixed-effect meta-analysis? I used already this
structure in a previous meta-analysis I conducted in the past,
but back then I was working with multiple treatments compared to
just one single control in each study.<br>
I see some similar meta-analysis in the past have used the
r-to-z transformed effect size and focused on the correlation
- in my case - between altitude and SLA, but not sure this is
what I would like to investigate in the first place...<br>
<br>
Hope I was clear, and my apologies if I was messy.<br>
<br>
Thanks a lot for reading this<br>
Gabriele<br>
<br>
<img src="cid:part3.975880A7.1AF050AF@imbi.uni-freiburg.de" width="440" height="437"><br>
<br>
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<pre class="m_-2443465987254378410moz-signature" cols="72">--
Dr. rer. nat. Gerta Rücker, Dipl.-Math.
Institute of Medical Biometry and Statistics,
Faculty of Medicine and Medical Center - University of Freiburg
<a href="https://maps.google.com/?q=Stefan-Meier-Str.+26,+D-79104+Freiburg,+Germany&entry=gmail&source=g">Stefan-Meier-Str. 26, D-79104 Freiburg, Germany</a>
Phone: +49/761/203-6673
Fax: +49/761/203-6680
Mail: <a class="m_-2443465987254378410moz-txt-link-abbreviated" href="mailto:ruecker@imbi.uni-freiburg.de" target="_blank">ruecker@imbi.uni-freiburg.de</a>
Homepage: <a class="m_-2443465987254378410moz-txt-link-freetext" href="https://portal.uni-freiburg.de/imbi/persons/ruecker?set_language=en" target="_blank">https://portal.uni-freiburg.<wbr>de/imbi/persons/ruecker?set_<wbr>language=en</a>
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