Lukasz Stasielowicz
Osnabr?ck University
Institute for Psychology
Research methods, psychological assessment, and evaluation
Lise-Meitner-Stra?e 3
49076 Osnabr?ck (Germany)
Twitter: https://twitter.com/l_stasielowicz
Tel.: +49 541 969-7735
On 08.05.2024 03:30, r-sig-meta-analysis-request at r-project.org wrote:
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> Today's Topics:
>
> 1. Online course: Meta-analysis in R (info at physalia-courses.org)
> 2. The P value of correlation coefficent in meta-analysis
> (Pengzhen Huang)
> 3. Correcting gain effects in nested studies (Zhouhan Jin)
> 4. Re: Correcting gain effects in nested studies (James Pustejovsky)
>
> ----------------------------------------------------------------------
>
> Message: 1
> Date: Tue, 7 May 2024 21:06:47 +0200 (CEST)
> From: "info at physalia-courses.org" <info at physalia-courses.org>
> To: r-sig-meta-analysis at r-project.org
> Subject: [R-meta] Online course: Meta-analysis in R
> Message-ID: <1715108807.553710121 at webmail.jimdo.com>
> Content-Type: text/plain; charset="utf-8"
>
>
> Dear all,
> There are only 2 seats left for our upcoming online course, META-ANALYSIS IN R.
> Dates: May 13-16, 2024
> Online: Accessible internationally
> This course covers:
> Systematic review and meta-analysis process
> Statistical analysis methods and interpretation
> Model diagnostics and sensitivity analyses
> Practical exercises with real meta-analytic datasets
> Prerequisites include basic statistical knowledge and familiarity with R. Resources for R preparation are provided.
> Course website: [ https://www.physalia-courses.org/courses-workshops/metain-r/ ]( https://www.physalia-courses.org/courses-workshops/metain-r/ )
>
> Best regards,
>
> Carlo
>
>
>
> --------------------
>
> Carlo Pecoraro, Ph.D
>
>
> Physalia-courses DIRECTOR
>
> info at physalia-courses.org
>
> mobile: +49 17645230846
>
>
>
>
>
> [[alternative HTML version deleted]]
>
>
>
>
> ------------------------------
>
> Message: 2
> Date: Wed, 8 May 2024 03:47:15 +0800 (GMT+08:00)
> From: "Pengzhen Huang" <maiqi1317 at 163.com>
> To: =?UTF-8?Q?r-sig-meta-analysis=40r-pr=E2=80=A6?=
> <r-sig-meta-analysis at r-project.org>
> Subject: [R-meta] The P value of correlation coefficent in
> meta-analysis
> Message-ID: <505c373a.1c5.18f54988e70.Coremail.maiqi1317 at 163.com>
> Content-Type: text/plain; charset="utf-8"
>
> Dear Community,
>
>
> I submitted a meta-analysis paper months ago and am now dealing with the reviewers' comments. In my research, the Pearson correlation coefficients are considered as effect size and put into the meta-analysis, and we regard the coefficients representing to what extent two variables are correlated with each other.
>
>
> On this point, one of reviewers argues that "as not all r values are significant, it does not make sense to put these non-significant correlation coefficients into the analysis".
>
>
> I?m not sure how to reply to this reviewer?s comment. But I guess this may be a common issue in meta-analysis. May I have some advice from you or could you tell me some references I should read through?
>
>
> I would greatly appreciate any suggestions you can provide!
>
>
>
>
> All the best,
> Pengzhen
>
>
>
>
>
>
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>
>
>
> ------------------------------
>
> Message: 3
> Date: Wed, 8 May 2024 00:14:11 +0000
> From: Zhouhan Jin <zjin65 at uwo.ca>
> To: R Special Interest Group for Meta-Analysis
> <r-sig-meta-analysis at r-project.org>
> Subject: [R-meta] Correcting gain effects in nested studies
> Message-ID: <d68c25db-a085-4e10-9e4b-d23de85a24ee at Spark>
> Content-Type: text/plain; charset="utf-8"
>
> Hello All,
>
> Hedges (2007) provides formulas for adjusting SMD effects (g) and their SEs for when primary studies have a nested design (below).
>
> But I want to compute gain effects (ex. SMCC in metafor::escalc) from my nested studies, not SMDs.
>
> So, how can I adjust my SMCCs and their SEs for nestedness in the primary studies?
>
>
> adjusted_g = g * sqrt(1 - ((2 * (n_bar - 1) * icc) /
> (n_cluster * n_bar - 2)))
>
> adjusted_SE = ((Nt+Nc)/(Nt*Nc))*(1 + ((n_bar- 1)*icc)) +
> ( g^2 * (
> (((N_tot -2)*(1-icc)^2 ) + (n_bar*(N_tot - 2*n_bar)*icc^2) +
> (2* (N_tot - 2*n_bar) * icc * (1 - icc)) ) /
> ((2* (N_tot-2)) * ( (N_tot-2) - (2* (n_bar-1)*icc) ))
> ) )
>
> Thanks a lot!
>
> Best wishes,
>
> Zhouhan
>
>
> [[alternative HTML version deleted]]
>
>
>
> ------------------------------
>
> Message: 4
> Date: Tue, 7 May 2024 20:30:33 -0500
> From: James Pustejovsky <jepusto at gmail.com>
> To: Zhouhan Jin <zjin65 at uwo.ca>
> Cc: R Special Interest Group for Meta-Analysis
> <r-sig-meta-analysis at r-project.org>
> Subject: Re: [R-meta] Correcting gain effects in nested studies
> Message-ID:
> <CAFUVuJyQmjrJJMFz3EZHGT+rR76YqKr1Zv6AVbvFqLcf+v_AOg at mail.gmail.com>
> Content-Type: text/plain; charset="utf-8"
>
> See Taylor, Pigott, and Williams (2022;
> https://doi.org/10.3102/0013189X211051319) for how to handle
> cluster-randomized trials that involve gain scores or covariate adjustment.
> They provide a shiny app too. The technical details are also described in
> Appendix E of the What Works Clearinghouse handbook (Version 5;
> https://ies.ed.gov/ncee/WWC/Docs/referenceresources/Final_WWC-HandbookVer5_0-0-508.pdf).
> See pp. 173-174
>
> The methods described in these sources are consistent with the "general
> recipe" for standardized mean difference estimates as described here:
> https://www.jepusto.com/alternative-formulas-for-the-smd/
>
> James
>
> On Tue, May 7, 2024 at 7:14?PM Zhouhan Jin <zjin65 at uwo.ca> wrote:
>
>> Hello All,
>>
>> Hedges (2007) provides formulas for adjusting SMD effects (g) and their
>> SEs for when primary studies have a nested design (below).
>>
>> But I want to compute gain effects (ex. SMCC in metafor::escalc) from my
>> nested studies, not SMDs.
>>
>> So, how can I adjust my SMCCs and their SEs for nestedness in the primary
>> studies?
>>
>>
>> adjusted_g = g * sqrt(1 - ((2 * (n_bar - 1) * icc) /
>> (n_cluster * n_bar - 2)))
>>
>> adjusted_SE = ((Nt+Nc)/(Nt*Nc))*(1 + ((n_bar- 1)*icc)) +
>> ( g^2 * (
>> (((N_tot -2)*(1-icc)^2 ) + (n_bar*(N_tot - 2*n_bar)*icc^2) +
>> (2* (N_tot - 2*n_bar) * icc * (1 - icc)) ) /
>> ((2* (N_tot-2)) * ( (N_tot-2) - (2* (n_bar-1)*icc) ))
>> ) )
>>
>> Thanks a lot!
>>
>> Best wishes,
>>
>> Zhouhan
>>
>>
>
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>
>
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