[R-meta] RV: meta analysis with standard deviation or standard errors
Martin Lobo
m|obo4370 @end|ng |rom hotm@||@com
Tue Apr 21 18:53:58 CEST 2020
Thank you very much Gerta.
I asked the question to see how I can solve two problems I have.
1- If I want to do an metaanalysis of mean difference analysis (Paired data, pre-post)
I have mean and sd pre and post, what methodd i use, MC , SMCC, etc
2- If I only have the mean and standard deviation as I do
3 - ri is always necessary or can be imputed in some way
4 - without ri the standard deviation of the mean difference can be estimated
5 - regarding question 4, both for independent samples and for paired samples
Sorry, but I am very confused, it is difficult to have all the data, and I know that the paired samples are treated differently
Best regard
Lorenzo Martín Lobo MTSAC, FACC, FESC
Especialista Jerarquizado en Cardiología
Jefe de Dpto Enf. Cardiovasculares y Cardiometabolismo Hospital Militar Campo de Mayo.
Jefe de Cardiología Hospital Militar Campo de Mayo
Ex Jefe de Unidad Coronaria Hospital Militar Campo de Mayo
Miembro Titular de la Sociedad Argentina de Cardiología
Fellow American College of Cardiology
Fellow European Society of Cardiology
Ex Miembro del Area de Investigación 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 Epidemiología y Prevención Cardiovascular de la SAC
Miembro Asesor del Consejo de Epidemiología y Prevención 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: Gerta Ruecker <ruecker using imbi.uni-freiburg.de>
Enviado: viernes, 17 de abril de 2020 08:12
Para: Martin Lobo <mlobo4370 using hotmail.com>; r-sig-meta-analysis using r-project.org <r-sig-meta-analysis using r-project.org>
Asunto: Re: [R-meta] meta analysis with standard deviation or standard errors
Dear Martin,
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).
Best,
Gerta
Am 17.04.2020 um 13:07 schrieb Martin Lobo:
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 <r-sig-meta-analysis-bounces using r-project.org><mailto:r-sig-meta-analysis-bounces using r-project.org> en nombre de r-sig-meta-analysis-request using r-project.org<mailto:r-sig-meta-analysis-request using r-project.org> <r-sig-meta-analysis-request using r-project.org><mailto:r-sig-meta-analysis-request using r-project.org>
Enviado: mi�rcoles, 15 de abril de 2020 07:00
Para: r-sig-meta-analysis using r-project.org<mailto:r-sig-meta-analysis using r-project.org> <r-sig-meta-analysis using r-project.org><mailto:r-sig-meta-analysis using r-project.org>
Asunto: R-sig-meta-analysis Digest, Vol 35, Issue 8
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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)"
<wolfgang.viechtbauer using maastrichtuniversity.nl><mailto:wolfgang.viechtbauer using maastrichtuniversity.nl>
To: Ju Lee <juhyung2 using stanford.edu><mailto:juhyung2 using stanford.edu>,
"r-sig-meta-analysis using r-project.org"<mailto:r-sig-meta-analysis using r-project.org>
<r-sig-meta-analysis using r-project.org><mailto:r-sig-meta-analysis using r-project.org>
Subject: Re: [R-meta] Dear Wolfgang
Message-ID: <b411740819d1411da87d505cdeceb3e6 using UM-MAIL3214.unimaas.nl><mailto:b411740819d1411da87d505cdeceb3e6 using UM-MAIL3214.unimaas.nl>
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
-----Original Message-----
From: Ju Lee [mailto:juhyung2 using stanford.edu]
Sent: Tuesday, 14 April, 2020 18:54
To: Viechtbauer, Wolfgang (SP); r-sig-meta-analysis using r-project.org<mailto:r-sig-meta-analysis using r-project.org>
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?
[[elided Hotmail spam]]
Best,
JU
________________________________________
From: Viechtbauer, Wolfgang (SP)
<wolfgang.viechtbauer using maastrichtuniversity.nl><mailto:wolfgang.viechtbauer using maastrichtuniversity.nl>
Sent: Tuesday, April 14, 2020 7:04 AM
To: Ju Lee <juhyung2 using stanford.edu><mailto:juhyung2 using stanford.edu>; r-sig-meta-analysis using r-project.org<mailto:r-sig-meta-analysis using r-project.org> <r-
sig-meta-analysis using r-project.org<mailto:sig-meta-analysis using r-project.org>>
Subject: RE: Dear Wolfgang
Dear Ju,
In principle, this might be of interest to you:
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<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>
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
-----Original Message-----
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces using r-
project.org]
On Behalf Of Ju Lee
Sent: Monday, 13 April, 2020 22:47
To: r-sig-meta-analysis using r-project.org<mailto:r-sig-meta-analysis using r-project.org>
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
to
compare effect sizes generated from their current mixed-effect meta-
analysis
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
comparison
(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
structures
used in different meta-analysis which may not be very apparent from method
description).
[[elided Hotmail spam]]
Best,
JU
------------------------------
Message: 2
Date: Wed, 15 Apr 2020 05:33:16 +0000
From: Ju Lee <juhyung2 using stanford.edu><mailto:juhyung2 using stanford.edu>
To: "Viechtbauer, Wolfgang (SP)"
<wolfgang.viechtbauer using maastrichtuniversity.nl><mailto:wolfgang.viechtbauer using maastrichtuniversity.nl>,
"r-sig-meta-analysis using r-project.org"<mailto:r-sig-meta-analysis using r-project.org>
<r-sig-meta-analysis using r-project.org><mailto:r-sig-meta-analysis using r-project.org>
Subject: Re: [R-meta] Dear Wolfgang
Message-ID:
<BYAPR02MB5559407370455A06F0B047A8F7DB0 using BYAPR02MB5559.namprd02.prod.outlook.com><mailto:BYAPR02MB5559407370455A06F0B047A8F7DB0 using BYAPR02MB5559.namprd02.prod.outlook.com>
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) <wolfgang.viechtbauer using maastrichtuniversity.nl><mailto:wolfgang.viechtbauer using maastrichtuniversity.nl>
Sent: Tuesday, April 14, 2020 1:43 PM
To: Ju Lee <juhyung2 using stanford.edu><mailto:juhyung2 using stanford.edu>; r-sig-meta-analysis using r-project.org<mailto:r-sig-meta-analysis using r-project.org> <r-sig-meta-analysis using r-project.org><mailto:r-sig-meta-analysis using r-project.org>
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
-----Original Message-----
From: Ju Lee [mailto:juhyung2 using stanford.edu]
Sent: Tuesday, 14 April, 2020 18:54
To: Viechtbauer, Wolfgang (SP); r-sig-meta-analysis using r-project.org<mailto:r-sig-meta-analysis using r-project.org>
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?
[[elided Hotmail spam]]
Best,
JU
________________________________________
From: Viechtbauer, Wolfgang (SP)
<wolfgang.viechtbauer using maastrichtuniversity.nl><mailto:wolfgang.viechtbauer using maastrichtuniversity.nl>
Sent: Tuesday, April 14, 2020 7:04 AM
To: Ju Lee <juhyung2 using stanford.edu><mailto:juhyung2 using stanford.edu>; r-sig-meta-analysis using r-project.org<mailto:r-sig-meta-analysis using r-project.org> <r-
sig-meta-analysis using r-project.org<mailto:sig-meta-analysis using r-project.org>>
Subject: RE: Dear Wolfgang
Dear Ju,
In principle, this might be of interest to you:
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<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>
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
-----Original Message-----
From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces using r-
project.org]
On Behalf Of Ju Lee
Sent: Monday, 13 April, 2020 22:47
To: r-sig-meta-analysis using r-project.org<mailto:r-sig-meta-analysis using r-project.org>
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
to
compare effect sizes generated from their current mixed-effect meta-
analysis
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
comparison
(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
structures
used in different meta-analysis which may not be very apparent from method
description).
[[elided Hotmail spam]]
Best,
JU
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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: ruecker using imbi.uni-freiburg.de<mailto:ruecker using imbi.uni-freiburg.de>
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