[R-meta] Aggregating dependent effect sizes for trimfill

Tue Jul 23 13:22:37 CEST 2024

Dear Andreas,

As indicated in the article mentioned by Michael Dewey, conducting more 
publication bias tests is not necessarily informative. Some approaches 
developed for simple meta-analytic models tend to perform poorly when 
dealing with dependent effects or even in simple meta-analytic models. 
Furthermore, caution is advised when aggregating effect sizes, as 
aggregating might mask heterogeneity. For example, an average effect of 
0 might mask the fact that there are both positive and negative effects 
and no null effects in the sample.

Carter, E. C., Schönbrodt, F. D., Gervais, W. M., & Hilgard, J. (2019).
Correcting for Bias in Psychology: A Comparison of Meta-Analytic
Methods. Advances in Methods and Practices in Psychological Science,
2(2), 115–144. https://doi.org/10.1177/2515245919847196

Renkewitz, F., & Keiner, M. (2019). How to detect publication bias in
psychological research: A comparative evaluation of six statistical
methods. Zeitschrift Fur Psychologie, 227(4), 261–279.
https://doi.org/10.1027/2151-2604/a000386

Since you mentioned that you are using the inverse standard error as a 
moderator, you might also want to take a look at the following 
recommendations to account for the fact that the standard errors are 
related to the effect size:

Pustejovsky, J. E., & Rodgers, M. A. (2019). Testing for funnel plot 
asymmetry of standardized mean differences. Research Synthesis Methods, 
10(1), 57–71. https://doi.org/10.1002/jrsm.1332

Best,
-- 
Lukasz Stasielowicz
Osnabrück University
Institute for Psychology
Research methods, psychological assessment, and evaluation
Lise-Meitner-Straße 3
49076 Osnabrück (Germany)
Twitter: https://twitter.com/l_stasielowicz
Tel.: +49 541 969-7735

On 23.07.2024 12:00, r-sig-meta-analysis-request using r-project.org wrote:
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>     1. Aggregating dependent effect sizes for trimfill (Andreas Voldstad)
>     2. Re: Aggregating dependent effect sizes for trimfill
>        (Michael Dewey)
> 
> ----------------------------------------------------------------------
> 
> Message: 1
> Date: Tue, 23 Jul 2024 08:59:05 +0000
> From: Andreas Voldstad <andreas.voldstad using kellogg.ox.ac.uk>
> To: Andreas Voldstad via R-sig-meta-analysis
> 	<r-sig-meta-analysis using r-project.org>
> Subject: [R-meta] Aggregating dependent effect sizes for trimfill
> Message-ID:
> 	<LO4P265MB350166966257359B9F71003BEDA92 using LO4P265MB3501.GBRP265.PROD.OUTLOOK.COM>
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> Content-Type: text/plain; charset="utf-8"
> 
> Dear Wolfgang, James and all,
> 
> I am doing a multilevel meta-analysis of SMDs, with partially empirical correlated and hierarchical effects ("PECHE"), corrected with cluster-robust variance estimation.
> 
> For assessment of publication bias risk, I have done Egger's regression by standardising the effect sizes and adding the inverse of their standard error as a moderator.
> 
> I would like to add some of the methods that are not compatible with dependent effect sizes, such as trim and fill, rank correlation test and perhaps stepwise models.
> 
> For visualisation, I have already aggregated the data based on this post: https://www.metafor-project.org/doku.php/tips:forest_plot_with_aggregated_values
> 
> And confirmed that running the rma.uni with REML on the aggregated data, and then applying RVE, yields practically the same results to the original multilevel model (i.e., up to .01 difference in the 95% CI).
> 
> I am wondering what you think in general about applying methods not suitable for rma.mv models, such as trimfill and ranktest, to this aggregated data (and the corresponding aggregated funnel plot)?
> 
> I performed rma.uni on the aggregated data, and passed it on to trimfill to get k0, a filled funnel plot, and a corrected effect.
> 
> If this is a valid approach, I am also wondering if there is a way to apply robust() to the trimfill corrected effect, so that it will be comparable to the effect from my original analysis?
> 
> 
> Best wishes,
> 
> Andreas Voldstad (he/him)
> PhD student in Psychiatry
> University of Oxford
> Please don�t feel obliged to read or respond to my email outside your own working hours.
> 
> 	[[alternative HTML version deleted]]
> 
> 
> 
> 
> ------------------------------
> 
> Message: 2
> Date: Tue, 23 Jul 2024 10:14:19 +0100
> From: Michael Dewey <lists using dewey.myzen.co.uk>
> To: R Special Interest Group for Meta-Analysis
> 	<r-sig-meta-analysis using r-project.org>
> Subject: Re: [R-meta] Aggregating dependent effect sizes for trimfill
> Message-ID: <0bcaa7e0-1a42-b32d-100d-9135375ee719 using dewey.myzen.co.uk>
> Content-Type: text/plain; charset="utf-8"; Format="flowed"
> 
> Dear Andreas
> 
> You might be interested in some work James and a co-author have
> published in this area.
> 
> https://psycnet.apa.org/doi/10.1037/met0000300
> 
> No doubt when the sun rises over the new world he will chip in.
> 
> Michael
> 
> On 23/07/2024 09:59, Andreas Voldstad via R-sig-meta-analysis wrote:
>> Dear Wolfgang, James and all,
>>
>> I am doing a multilevel meta-analysis of SMDs, with partially empirical correlated and hierarchical effects ("PECHE"), corrected with cluster-robust variance estimation.
>>
>> For assessment of publication bias risk, I have done Egger's regression by standardising the effect sizes and adding the inverse of their standard error as a moderator.
>>
>> I would like to add some of the methods that are not compatible with dependent effect sizes, such as trim and fill, rank correlation test and perhaps stepwise models.
>>
>> For visualisation, I have already aggregated the data based on this post: https://www.metafor-project.org/doku.php/tips:forest_plot_with_aggregated_values
>>
>> And confirmed that running the rma.uni with REML on the aggregated data, and then applying RVE, yields practically the same results to the original multilevel model (i.e., up to .01 difference in the 95% CI).
>>
>> I am wondering what you think in general about applying methods not suitable for rma.mv models, such as trimfill and ranktest, to this aggregated data (and the corresponding aggregated funnel plot)?
>>
>> I performed rma.uni on the aggregated data, and passed it on to trimfill to get k0, a filled funnel plot, and a corrected effect.
>>
>> If this is a valid approach, I am also wondering if there is a way to apply robust() to the trimfill corrected effect, so that it will be comparable to the effect from my original analysis?
>>
>>
>> Best wishes,
>>
>> Andreas Voldstad (he/him)
>> PhD student in Psychiatry
>> University of Oxford
>> Please don�t feel obliged to read or respond to my email outside your own working hours.
>>
>> 	[[alternative HTML version deleted]]
>>
>>
>>
>>
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