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<p>Dear Martin,</p>
<p><br>
</p>
<p>Sorry for the delay. The problem is that the mean and sd of pre
and post do not suffice to know the sd of the pairwise
differences, except one makes some assumptions about the
intraindividual pre-post correlation. See the attached R code
PrePost.R for illustration.</p>
<p><br>
</p>
<p>Do you mean by ri the correlation coefficients? If you impute
them (say, 0.5), you may analyse the pre-post changes, but you
should have some (external) evidence for using a certain value.</p>
<p><br>
</p>
<p>I am not sure about each one of your 5 points below, see inline
below.</p>
<p><br>
</p>
<p>Best,</p>
<p>Gerta<br>
</p>
<p><br>
</p>
<p><br>
</p>
<div class="moz-cite-prefix">Am 17.04.2020 um 14:22 schrieb Martin
Lobo:<br>
</div>
<blockquote type="cite"
cite="mid:MN2PR20MB281489CC5136C7CFD921DA7EACD90@MN2PR20MB2814.namprd20.prod.outlook.com">
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Thank you very much Gerta.</div>
<div style="font-family: Calibri, Helvetica, sans-serif;
font-size: 12pt; color: rgb(0, 0, 0);">
<br>
</div>
<div style="font-family: Calibri, Helvetica, sans-serif;
font-size: 12pt; color: rgb(0, 0, 0);">
I asked the question to see how I can solve two problems I have.<br>
</div>
<div style="font-family: Calibri, Helvetica, sans-serif;
font-size: 12pt; color: rgb(0, 0, 0);">
<br>
</div>
<div style="font-family: Calibri, Helvetica, sans-serif;
font-size: 12pt; color: rgb(0, 0, 0);">
1- If I want to do an metaanalysis of mean difference analysis
(Paired data, pre-post)</div>
<div style="font-family: Calibri, Helvetica, sans-serif;
font-size: 12pt; color: rgb(0, 0, 0);">
I have mean and sd pre and post, what methodd i use, MC , SMCC,
etc</div>
</blockquote>
What is MC, SMCC? I don't know for what these abbreviations stand.
Otherwise, see above.<br>
<blockquote type="cite"
cite="mid:MN2PR20MB281489CC5136C7CFD921DA7EACD90@MN2PR20MB2814.namprd20.prod.outlook.com">
<div style="font-family: Calibri, Helvetica, sans-serif;
font-size: 12pt; color: rgb(0, 0, 0);">
<br>
</div>
<div style="font-family: Calibri, Helvetica, sans-serif;
font-size: 12pt; color: rgb(0, 0, 0);">
2- If I only have the mean and standard deviation as I do</div>
</blockquote>
See above.<br>
<blockquote type="cite"
cite="mid:MN2PR20MB281489CC5136C7CFD921DA7EACD90@MN2PR20MB2814.namprd20.prod.outlook.com">
<div style="font-family: Calibri, Helvetica, sans-serif;
font-size: 12pt; color: rgb(0, 0, 0);">
<br>
</div>
<div style="font-family: Calibri, Helvetica, sans-serif;
font-size: 12pt; color: rgb(0, 0, 0);">
3 - ri is always necessary or can be imputed in some way</div>
</blockquote>
See also above<br>
<blockquote type="cite"
cite="mid:MN2PR20MB281489CC5136C7CFD921DA7EACD90@MN2PR20MB2814.namprd20.prod.outlook.com">
<div style="font-family: Calibri, Helvetica, sans-serif;
font-size: 12pt; color: rgb(0, 0, 0);">
<br>
</div>
<div style="font-family: Calibri, Helvetica, sans-serif;
font-size: 12pt; color: rgb(0, 0, 0);">
4 - without ri the standard deviation of the mean difference can
be estimated</div>
</blockquote>
Not without knowing or making assumptions about the correlation, as
said above.<br>
<blockquote type="cite"
cite="mid:MN2PR20MB281489CC5136C7CFD921DA7EACD90@MN2PR20MB2814.namprd20.prod.outlook.com">
<div style="font-family: Calibri, Helvetica, sans-serif;
font-size: 12pt; color: rgb(0, 0, 0);">
<br>
</div>
<div style="font-family: Calibri, Helvetica, sans-serif;
font-size: 12pt; color: rgb(0, 0, 0);">
5 - regarding question 4, both for independent samples and for
paired samples</div>
</blockquote>
<p>For independent samples it is different, because for differences
of independent means we have:</p>
<p><br>
</p>
<p>sd(X + Y) = sqrt(var(X + Y)) = sqrt(var(X) + var(Y)) =
sqrt(sd(X)^2 + sd(Y)^2)</p>
<p><br>
</p>
<p>For paired (more general. correlated) variables:</p>
<p><br>
</p>
<p>sd(X + Y) = sqrt(var(X) + var(Y) - 2Cov(X,Y))</p>
<p><br>
</p>
<blockquote type="cite"
cite="mid:MN2PR20MB281489CC5136C7CFD921DA7EACD90@MN2PR20MB2814.namprd20.prod.outlook.com">
<div style="font-family: Calibri, Helvetica, sans-serif;
font-size: 12pt; color: rgb(0, 0, 0);">
<br>
</div>
<br>
<div>
<div style="font-family:Calibri,Helvetica,sans-serif;
font-size:12pt; color:rgb(0,0,0)">
<br>
</div>
<hr tabindex="-1" style="display:inline-block; width:98%">
<div id="divRplyFwdMsg" dir="ltr"><font style="font-size:11pt"
face="Calibri, sans-serif" color="#000000"><b>De:</b> Gerta
Ruecker <a class="moz-txt-link-rfc2396E" href="mailto:ruecker@imbi.uni-freiburg.de"><ruecker@imbi.uni-freiburg.de></a><br>
<b>Enviado:</b> viernes, 17 de abril de 2020 08:12<br>
<b>Para:</b> Martin Lobo <a class="moz-txt-link-rfc2396E" href="mailto:mlobo4370@hotmail.com"><mlobo4370@hotmail.com></a>;
<a class="moz-txt-link-abbreviated" href="mailto:r-sig-meta-analysis@r-project.org">r-sig-meta-analysis@r-project.org</a>
<a class="moz-txt-link-rfc2396E" href="mailto:r-sig-meta-analysis@r-project.org"><r-sig-meta-analysis@r-project.org></a><br>
<b>Asunto:</b> Re: [R-meta] meta analysis with standard
deviation or standard errors</font>
<div> </div>
</div>
<div>
<p>Dear Martin,</p>
<p>The answer is no. The standard error is not a measure of
dispersion of the data, but a measure of the imprecision of
estimation. A large standard error may come from large
variability between data, but also from small sample size.
The standard error becomes always small if the sample size
becomes large (law of large numbers).</p>
<p>Best,</p>
<p>Gerta<br>
</p>
<div class="x_moz-cite-prefix">Am 17.04.2020 um 13:07 schrieb
Martin Lobo:<br>
</div>
<blockquote type="cite">
<pre class="x_moz-quote-pre">Hello everyone !
I wanted to know if it is possible to use the standard error instead of the standard deviation as a measure of dispersion.
using the MD or SMD method for independent samples.
If this is possible, there would be some difference in the conclusions.
Thank you so much
Lorenzo Mart�n Lobo MTSAC, FACC, FESC
Especialista Jerarquizado en Cardiolog�a
Jefe de Dpto Enf. Cardiovasculares y Cardiometabolismo Hospital Militar Campo de Mayo.
Jefe de Cardiolog�a Hospital Militar Campo de Mayo
Ex Jefe de Unidad Coronaria Hospital Militar Campo de Mayo
Miembro Titular de la Sociedad Argentina de Cardiolog�a
Fellow American College of Cardiology
Fellow European Society of Cardiology
Ex Miembro del Area de Investigaci�n de la SAC
Ex Director del Consejo de Aterosclerosis y Trombosis de la SAC
Miembro Asesor del Consejo de Aterosclerosis y Trombosis de la SAC
Ex Director del Consejo de Epidemiolog�a y Prevenci�n Cardiovascular de la SAC
Miembro Asesor del Consejo de Epidemiolog�a y Prevenci�n Cardiovascular de la SAC
Experto en Lipidos de la Sociedad Argentina de Lipidos.
Miembro de la Sociedad Argentina de Lipidos.
Instructor de ACLS de la American Heart Association
________________________________
De: R-sig-meta-analysis <a class="x_moz-txt-link-rfc2396E" href="mailto:r-sig-meta-analysis-bounces@r-project.org" moz-do-not-send="true"><r-sig-meta-analysis-bounces@r-project.org></a> en nombre de <a class="x_moz-txt-link-abbreviated" href="mailto:r-sig-meta-analysis-request@r-project.org" moz-do-not-send="true">r-sig-meta-analysis-request@r-project.org</a> <a class="x_moz-txt-link-rfc2396E" href="mailto:r-sig-meta-analysis-request@r-project.org" moz-do-not-send="true"><r-sig-meta-analysis-request@r-project.org></a>
Enviado: mi�rcoles, 15 de abril de 2020 07:00
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Today's Topics:
1. Re: Dear Wolfgang (Viechtbauer, Wolfgang (SP))
2. Re: Dear Wolfgang (Ju Lee)
----------------------------------------------------------------------
Message: 1
Date: Tue, 14 Apr 2020 20:43:51 +0000
From: "Viechtbauer, Wolfgang (SP)"
<a class="x_moz-txt-link-rfc2396E" href="mailto:wolfgang.viechtbauer@maastrichtuniversity.nl" moz-do-not-send="true"><wolfgang.viechtbauer@maastrichtuniversity.nl></a>
To: Ju Lee <a class="x_moz-txt-link-rfc2396E" href="mailto:juhyung2@stanford.edu" moz-do-not-send="true"><juhyung2@stanford.edu></a>,
<a class="x_moz-txt-link-rfc2396E" href="mailto:r-sig-meta-analysis@r-project.org" moz-do-not-send="true">"r-sig-meta-analysis@r-project.org"</a>
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Subject: Re: [R-meta] Dear Wolfgang
Message-ID: <a class="x_moz-txt-link-rfc2396E" href="mailto:b411740819d1411da87d505cdeceb3e6@UM-MAIL3214.unimaas.nl" moz-do-not-send="true"><b411740819d1411da87d505cdeceb3e6@UM-MAIL3214.unimaas.nl></a>
Content-Type: text/plain; charset="iso-8859-1"
Yes, if the effect size measure is the same, one can make such a comparison. Also, there should not be any overlap in the studies included in the two meta-analyses (as otherwise the two estimates are not independent, as assumed by the test). And yes, you don't need sample sizes or tau^2 values or anything else - just the two estimates and their corresponding standard errors. And it doesn't depend on what random effects structure was used in the two meta-analyses -- assuming that the structures used in the two meta-analyses were appropriate for the studies at hand.
Best,
Wolfgang
</pre>
<blockquote type="cite">
<pre class="x_moz-quote-pre">-----Original Message-----
From: Ju Lee [<a class="x_moz-txt-link-freetext" href="mailto:juhyung2@stanford.edu" moz-do-not-send="true">mailto:juhyung2@stanford.edu</a>]
Sent: Tuesday, 14 April, 2020 18:54
To: Viechtbauer, Wolfgang (SP); <a class="x_moz-txt-link-abbreviated" href="mailto:r-sig-meta-analysis@r-project.org" moz-do-not-send="true">r-sig-meta-analysis@r-project.org</a>
Subject: Re: Dear Wolfgang
Dear Wolfgang,
Thanks for your insights.
I am reaching out to my colleagues to see how they have made such
transformation.
In the meantime, based on the information that you have sent, it is possible
to compare two different meta-analyses if they are using the same effect
size, say lnRR? and this wald-type test can be performed only with grand
mean effect sizes and their standard error, without sample sizes or tau
value, if I understood correctly?
How would this approach be actually applicable to publications that
seemingly used similar mixed-effect models but there is no guarantee that
random effect structures are standardized between the two?
</pre>
</blockquote>
<pre class="x_moz-quote-pre">[[elided Hotmail spam]]
</pre>
<blockquote type="cite">
<pre class="x_moz-quote-pre">Best,
JU
________________________________________
From: Viechtbauer, Wolfgang (SP)
<a class="x_moz-txt-link-rfc2396E" href="mailto:wolfgang.viechtbauer@maastrichtuniversity.nl" moz-do-not-send="true"><wolfgang.viechtbauer@maastrichtuniversity.nl></a>
Sent: Tuesday, April 14, 2020 7:04 AM
To: Ju Lee <a class="x_moz-txt-link-rfc2396E" href="mailto:juhyung2@stanford.edu" moz-do-not-send="true"><juhyung2@stanford.edu></a>; <a class="x_moz-txt-link-abbreviated" href="mailto:r-sig-meta-analysis@r-project.org" moz-do-not-send="true">r-sig-meta-analysis@r-project.org</a> <r-
<a class="x_moz-txt-link-abbreviated" href="mailto:sig-meta-analysis@r-project.org" moz-do-not-send="true">sig-meta-analysis@r-project.org</a>>
Subject: RE: Dear Wolfgang
Dear Ju,
In principle, this might be of interest to you:
<a class="x_moz-txt-link-freetext" href="https://nam12.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.metafor-project.org%2Fdoku.php%2Ftips%3Acomp_two_independent_estimates&data=02%7C01%7C%7Cb7f3d3e47adf442bf2e108d7e2c043b0%7C84df9e7fe9f640afb435aaaaaaaaaaaa%7C1%7C0%7C637227187709261011&sdata=3NgDYQD8icHrgAbpPUh7GV7mdYmYMyiHvPlQ2j2nLMg%3D&reserved=0" originalsrc="http://www.metafor-project.org/doku.php/tips:comp_two_independent_estimates" shash="E6pYXn3VZKIyoc7HDBIBHA2mqrr4RxOb+gKwOWJHdJsjFlGLM6EZzaMqPs5czM8ZzLJ+7axb9glJvYD3c85x3TovLcg4ScwLDIFh76F4SOodFt3LCfGdH4L/5L88tNlqRgsPcfs4xQLVY+546Ab9yXSDq7pBSMALh3/eX19GDG8=" moz-do-not-send="true">https://nam01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.metafor-project.org%2Fdoku.php%2Ftips%3Acomp_two_independent_estimates&data=02%7C01%7C%7C7f93a72da7b64707fe6d08d7e12439ac%7C84df9e7fe9f640afb435aaaaaaaaaaaa%7C1%7C0%7C637225418004815037&sdata=Tqgh0WpvUo70JTaihNWcZcbVQCQRpbprCYAxGKtlBGY%3D&reserved=0</a>
However, a standardized mean difference is given by (m1-m2)/sd, while a
(log) response ratio is log(m1/m2). I see no sensible way of converting the
former to the later.
Best,
Wolfgang
</pre>
<blockquote type="cite">
<pre class="x_moz-quote-pre">-----Original Message-----
From: R-sig-meta-analysis [<a class="x_moz-txt-link-freetext" href="mailto:r-sig-meta-analysis-bounces@r" moz-do-not-send="true">mailto:r-sig-meta-analysis-bounces@r</a>-
</pre>
</blockquote>
<pre class="x_moz-quote-pre">project.org]
</pre>
<blockquote type="cite">
<pre class="x_moz-quote-pre">On Behalf Of Ju Lee
Sent: Monday, 13 April, 2020 22:47
To: <a class="x_moz-txt-link-abbreviated" href="mailto:r-sig-meta-analysis@r-project.org" moz-do-not-send="true">r-sig-meta-analysis@r-project.org</a>
Subject: [R-meta] Dear Wolfgang
Dear Wolfgang,
I hope you are doing well.
My research group is currently working on a project where they are trying
</pre>
</blockquote>
<pre class="x_moz-quote-pre">to
</pre>
<blockquote type="cite">
<pre class="x_moz-quote-pre">compare effect sizes generated from their current mixed-effect meta-
</pre>
</blockquote>
<pre class="x_moz-quote-pre">analysis
</pre>
<blockquote type="cite">
<pre class="x_moz-quote-pre">with effect sizes (based on similar response variables) calculated in other
meta-analysis publications.
We are currently using log response ratio and are trying to make some
statement or analysis to compare our grand mean effect sizes with other
studies. In more details, we are examining how herbivorous animal control
plant growth in degraded environment. Now, there is already a meta-analysis
out there that has examined this (in comparable manner) in natural
environment as opposed to our study.
My colleagues want to know if there is a way to make some type of
</pre>
</blockquote>
<pre class="x_moz-quote-pre">comparison
</pre>
<blockquote type="cite">
<pre class="x_moz-quote-pre">(ex. whether responses are stronger in degraded vs. natural environemnts)
between two effect sizes from these different studies using statistical
approaches.
So far what they have from other meta-analysis publication is grand mean
hedges'd and var which they transformed to lnRR and var in hopes to compare
with our lnRR effect sizes.
My view is that this is not possible unless we can have their actual raw
dataset and run a whole new model combining with our original raw dataset.
But I wanted to reach out to you and the community if there is an
alternative approaches to compare mean effect sizes among different meta-
analysis which are assumed to have used similar approaches in study
selection and models (another issue being different random effect
</pre>
</blockquote>
<pre class="x_moz-quote-pre">structures
</pre>
<blockquote type="cite">
<pre class="x_moz-quote-pre">used in different meta-analysis which may not be very apparent from method
description).
</pre>
</blockquote>
</blockquote>
<pre class="x_moz-quote-pre">[[elided Hotmail spam]]
</pre>
<blockquote type="cite">
<blockquote type="cite">
<pre class="x_moz-quote-pre">Best,
JU
</pre>
</blockquote>
</blockquote>
<pre class="x_moz-quote-pre">------------------------------
Message: 2
Date: Wed, 15 Apr 2020 05:33:16 +0000
From: Ju Lee <a class="x_moz-txt-link-rfc2396E" href="mailto:juhyung2@stanford.edu" moz-do-not-send="true"><juhyung2@stanford.edu></a>
To: "Viechtbauer, Wolfgang (SP)"
<a class="x_moz-txt-link-rfc2396E" href="mailto:wolfgang.viechtbauer@maastrichtuniversity.nl" moz-do-not-send="true"><wolfgang.viechtbauer@maastrichtuniversity.nl></a>,
<a class="x_moz-txt-link-rfc2396E" href="mailto:r-sig-meta-analysis@r-project.org" moz-do-not-send="true">"r-sig-meta-analysis@r-project.org"</a>
<a class="x_moz-txt-link-rfc2396E" href="mailto:r-sig-meta-analysis@r-project.org" moz-do-not-send="true"><r-sig-meta-analysis@r-project.org></a>
Subject: Re: [R-meta] Dear Wolfgang
Message-ID:
<a class="x_moz-txt-link-rfc2396E" href="mailto:BYAPR02MB5559407370455A06F0B047A8F7DB0@BYAPR02MB5559.namprd02.prod.outlook.com" moz-do-not-send="true"><BYAPR02MB5559407370455A06F0B047A8F7DB0@BYAPR02MB5559.namprd02.prod.outlook.com></a>
Content-Type: text/plain; charset="utf-8"
Dear Wolfgang,
[[elided Hotmail spam]]
I am not sure how my colleagues have transformed hedges' d to lnRR, based on what sources, but I will reach out again once I have more details. I, too, have not known if there is a way to make such effect size transformation.
Thank you very much!
Best wishes,
JU
________________________________
From: Viechtbauer, Wolfgang (SP) <a class="x_moz-txt-link-rfc2396E" href="mailto:wolfgang.viechtbauer@maastrichtuniversity.nl" moz-do-not-send="true"><wolfgang.viechtbauer@maastrichtuniversity.nl></a>
Sent: Tuesday, April 14, 2020 1:43 PM
To: Ju Lee <a class="x_moz-txt-link-rfc2396E" href="mailto:juhyung2@stanford.edu" moz-do-not-send="true"><juhyung2@stanford.edu></a>; <a class="x_moz-txt-link-abbreviated" href="mailto:r-sig-meta-analysis@r-project.org" moz-do-not-send="true">r-sig-meta-analysis@r-project.org</a> <a class="x_moz-txt-link-rfc2396E" href="mailto:r-sig-meta-analysis@r-project.org" moz-do-not-send="true"><r-sig-meta-analysis@r-project.org></a>
Subject: RE: Dear Wolfgang
Yes, if the effect size measure is the same, one can make such a comparison. Also, there should not be any overlap in the studies included in the two meta-analyses (as otherwise the two estimates are not independent, as assumed by the test). And yes, you don't need sample sizes or tau^2 values or anything else - just the two estimates and their corresponding standard errors. And it doesn't depend on what random effects structure was used in the two meta-analyses -- assuming that the structures used in the two meta-analyses were appropriate for the studies at hand.
Best,
Wolfgang
</pre>
<blockquote type="cite">
<pre class="x_moz-quote-pre">-----Original Message-----
From: Ju Lee [<a class="x_moz-txt-link-freetext" href="mailto:juhyung2@stanford.edu" moz-do-not-send="true">mailto:juhyung2@stanford.edu</a>]
Sent: Tuesday, 14 April, 2020 18:54
To: Viechtbauer, Wolfgang (SP); <a class="x_moz-txt-link-abbreviated" href="mailto:r-sig-meta-analysis@r-project.org" moz-do-not-send="true">r-sig-meta-analysis@r-project.org</a>
Subject: Re: Dear Wolfgang
Dear Wolfgang,
Thanks for your insights.
I am reaching out to my colleagues to see how they have made such
transformation.
In the meantime, based on the information that you have sent, it is possible
to compare two different meta-analyses if they are using the same effect
size, say lnRR? and this wald-type test can be performed only with grand
mean effect sizes and their standard error, without sample sizes or tau
value, if I understood correctly?
How would this approach be actually applicable to publications that
seemingly used similar mixed-effect models but there is no guarantee that
random effect structures are standardized between the two?
</pre>
</blockquote>
<pre class="x_moz-quote-pre">[[elided Hotmail spam]]
</pre>
<blockquote type="cite">
<pre class="x_moz-quote-pre">Best,
JU
________________________________________
From: Viechtbauer, Wolfgang (SP)
<a class="x_moz-txt-link-rfc2396E" href="mailto:wolfgang.viechtbauer@maastrichtuniversity.nl" moz-do-not-send="true"><wolfgang.viechtbauer@maastrichtuniversity.nl></a>
Sent: Tuesday, April 14, 2020 7:04 AM
To: Ju Lee <a class="x_moz-txt-link-rfc2396E" href="mailto:juhyung2@stanford.edu" moz-do-not-send="true"><juhyung2@stanford.edu></a>; <a class="x_moz-txt-link-abbreviated" href="mailto:r-sig-meta-analysis@r-project.org" moz-do-not-send="true">r-sig-meta-analysis@r-project.org</a> <r-
<a class="x_moz-txt-link-abbreviated" href="mailto:sig-meta-analysis@r-project.org" moz-do-not-send="true">sig-meta-analysis@r-project.org</a>>
Subject: RE: Dear Wolfgang
Dear Ju,
In principle, this might be of interest to you:
<a class="x_moz-txt-link-freetext" href="https://nam12.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.metafor-project.org%2Fdoku.php%2Ftips%3Acomp_two_independent_estimates&data=02%7C01%7C%7Cb7f3d3e47adf442bf2e108d7e2c043b0%7C84df9e7fe9f640afb435aaaaaaaaaaaa%7C1%7C0%7C637227187709261011&sdata=3NgDYQD8icHrgAbpPUh7GV7mdYmYMyiHvPlQ2j2nLMg%3D&reserved=0" originalsrc="http://www.metafor-project.org/doku.php/tips:comp_two_independent_estimates" shash="E6pYXn3VZKIyoc7HDBIBHA2mqrr4RxOb+gKwOWJHdJsjFlGLM6EZzaMqPs5czM8ZzLJ+7axb9glJvYD3c85x3TovLcg4ScwLDIFh76F4SOodFt3LCfGdH4L/5L88tNlqRgsPcfs4xQLVY+546Ab9yXSDq7pBSMALh3/eX19GDG8=" moz-do-not-send="true">https://nam01.safelinks.protection.outlook.com/?url=http%3A%2F%2Fwww.metafor-project.org%2Fdoku.php%2Ftips%3Acomp_two_independent_estimates&data=02%7C01%7C%7C7f93a72da7b64707fe6d08d7e12439ac%7C84df9e7fe9f640afb435aaaaaaaaaaaa%7C1%7C0%7C637225418004815037&sdata=Tqgh0WpvUo70JTaihNWcZcbVQCQRpbprCYAxGKtlBGY%3D&reserved=0</a>
However, a standardized mean difference is given by (m1-m2)/sd, while a
(log) response ratio is log(m1/m2). I see no sensible way of converting the
former to the later.
Best,
Wolfgang
</pre>
<blockquote type="cite">
<pre class="x_moz-quote-pre">-----Original Message-----
From: R-sig-meta-analysis [<a class="x_moz-txt-link-freetext" href="mailto:r-sig-meta-analysis-bounces@r" moz-do-not-send="true">mailto:r-sig-meta-analysis-bounces@r</a>-
</pre>
</blockquote>
<pre class="x_moz-quote-pre">project.org]
</pre>
<blockquote type="cite">
<pre class="x_moz-quote-pre">On Behalf Of Ju Lee
Sent: Monday, 13 April, 2020 22:47
To: <a class="x_moz-txt-link-abbreviated" href="mailto:r-sig-meta-analysis@r-project.org" moz-do-not-send="true">r-sig-meta-analysis@r-project.org</a>
Subject: [R-meta] Dear Wolfgang
Dear Wolfgang,
I hope you are doing well.
My research group is currently working on a project where they are trying
</pre>
</blockquote>
<pre class="x_moz-quote-pre">to
</pre>
<blockquote type="cite">
<pre class="x_moz-quote-pre">compare effect sizes generated from their current mixed-effect meta-
</pre>
</blockquote>
<pre class="x_moz-quote-pre">analysis
</pre>
<blockquote type="cite">
<pre class="x_moz-quote-pre">with effect sizes (based on similar response variables) calculated in other
meta-analysis publications.
We are currently using log response ratio and are trying to make some
statement or analysis to compare our grand mean effect sizes with other
studies. In more details, we are examining how herbivorous animal control
plant growth in degraded environment. Now, there is already a meta-analysis
out there that has examined this (in comparable manner) in natural
environment as opposed to our study.
My colleagues want to know if there is a way to make some type of
</pre>
</blockquote>
<pre class="x_moz-quote-pre">comparison
</pre>
<blockquote type="cite">
<pre class="x_moz-quote-pre">(ex. whether responses are stronger in degraded vs. natural environemnts)
between two effect sizes from these different studies using statistical
approaches.
So far what they have from other meta-analysis publication is grand mean
hedges'd and var which they transformed to lnRR and var in hopes to compare
with our lnRR effect sizes.
My view is that this is not possible unless we can have their actual raw
dataset and run a whole new model combining with our original raw dataset.
But I wanted to reach out to you and the community if there is an
alternative approaches to compare mean effect sizes among different meta-
analysis which are assumed to have used similar approaches in study
selection and models (another issue being different random effect
</pre>
</blockquote>
<pre class="x_moz-quote-pre">structures
</pre>
<blockquote type="cite">
<pre class="x_moz-quote-pre">used in different meta-analysis which may not be very apparent from method
description).
</pre>
</blockquote>
</blockquote>
<pre class="x_moz-quote-pre">[[elided Hotmail spam]]
</pre>
<blockquote type="cite">
<blockquote type="cite">
<pre class="x_moz-quote-pre">Best,
JU
</pre>
</blockquote>
</blockquote>
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<pre class="x_moz-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
Stefan-Meier-Str. 26, D-79104 Freiburg, Germany
Phone: +49/761/203-6673
Fax: +49/761/203-6680
Mail: <a class="x_moz-txt-link-abbreviated" href="mailto:ruecker@imbi.uni-freiburg.de" moz-do-not-send="true">ruecker@imbi.uni-freiburg.de</a>
Homepage: <a class="x_moz-txt-link-freetext" href="https://nam12.safelinks.protection.outlook.com/?url=https%3A%2F%2Fwww.imbi.uni-freiburg.de%2Fpersons%2Fruecker%2Fperson_view&data=02%7C01%7C%7Cb7f3d3e47adf442bf2e108d7e2c043b0%7C84df9e7fe9f640afb435aaaaaaaaaaaa%7C1%7C0%7C637227187709281000&sdata=mjpFpTJU5Q9MRANTuieMFUBB6wQnN%2Bmb3mGHWU1Wq0c%3D&reserved=0" originalsrc="https://www.imbi.uni-freiburg.de/persons/ruecker/person_view" shash="Xl3z3uS1PqqM5JASbmY1KYEFVzRm76Rk9//K5OwhaKBKnl69/dZa/dRjbetfu86QoWQl7PXc3WXOP3UTQM8HmfN5SQrQ49c/ViTXLq+I+o6rS8wEHeF+MzpusrDoJ9pfWbTnqmUspbXlcZvxaiMJFxtkYxLRl+iSWGlqdBDimds=" moz-do-not-send="true">https://www.imbi.uni-freiburg.de/persons/ruecker/person_view</a>
</pre>
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<pre class="moz-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
Stefan-Meier-Str. 26, D-79104 Freiburg, Germany
Phone: +49/761/203-6673
Fax: +49/761/203-6680
Mail: <a class="moz-txt-link-abbreviated" href="mailto:ruecker@imbi.uni-freiburg.de">ruecker@imbi.uni-freiburg.de</a>
Homepage: <a class="moz-txt-link-freetext" href="https://www.imbi.uni-freiburg.de/persons/ruecker/person_view">https://www.imbi.uni-freiburg.de/persons/ruecker/person_view</a>
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