[R-meta] Pooling studies with binary and continuous outcomes

[Lukasz Stasielowicz]

Dear Graham,

yes, it is common that meta-analysts convert effect sizes, e.g. r --> d
Respective formulas are reported in meta-analytic textbooks or 
individual papers.

There are also some online calculators for effect sizes, e.g. 
https://www.campbellcollaboration.org/escalc/html/EffectSizeCalculator-Home.php

There is also at least one R package - compute.es
https://cran.r-project.org/web/packages/compute.es/compute.es.pdf

Sometimes meta-analysts conduct a moderator analysis and compare 
converted effect sizes (e.g., r-->d) with effect sizes that didn't 
require converting (e.g. d), in order to check the influence of pooling 
different designs/effects.


Best,
Lukasz
-- 
Lukasz Stasielowicz
Osnabrück University
Institute for Psychology
Research methods, psychological assessment, and evaluation
Seminarstraße 20
49074 Osnabrück (Germany)

Am 28.10.2021 um 12:00 schrieb r-sig-meta-analysis-request using r-project.org:
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>     1. Pooling studies with binary and continuous outcomes
>        (Blackman, Graham)
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> ----------------------------------------------------------------------
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> Message: 1
> Date: Wed, 27 Oct 2021 20:13:55 +0000
> From: "Blackman, Graham" <graham.blackman using kcl.ac.uk>
> To: "r-sig-meta-analysis using r-project.org"
> 	<r-sig-meta-analysis using r-project.org>
> Subject: [R-meta] Pooling studies with binary and continuous outcomes
> Message-ID:
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> 
> Dear R Special Interest Group for Meta-Analysis,
> 
> I hope this email find you all well.
> 
> I'm currently completing a meta-analysis (using the metafor package) looking at biological predictors of treatment response in patients suffering from psychosis. The predictor variable is recorded on a continuous scale.
> 
> Here�s the challenge�
> 
> Some studies report outcome as a binary variable (responder vs non-responders) and others report outcome as a continuous variable (% change in symptoms). For the latter, studies typically report the correlation coefficient. I would like to combine these different study designs to increase the statistical power.
> 
> Here�s my question�
> 
> Is it generally appropriate to convert the effect sizes for a correlation analysis (Pearson�s correlation coefficient) to Cohen's D?
> 
> If so, that solves the dilemma � if not, are they are other solutions?
> 
> Any advice greatly appreciated!
> 
> Best wishes,
> 
> Graham
> 
> 
> --------------------------------------------------------------
> 
> Dr Graham Blackman BSc MBChB MRCPsych
> Clinical Research Fellow
> Section of Cognitive Neuropsychiatry, Psychosis Studies, 6th Floor
> Institute of Psychiatry, Psychology and Neuroscience, 16 De Crespigny Park, Camberwell, London SE5 8AF
> 
> Email: graham.blackman using kcl.ac.uk<mailto:graham.blackman using kcl.ac.uk>
> Telephone: 02078485228
> 
> 
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