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

[Michael Dewey]

Dear Graham

It is not entirely clear to me what data each primary study is giving 
you. Comments below in-line

On 27/10/2021 21:13, Blackman, Graham wrote:
> 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)

So what do they report, mean of the predictor in each group with its 
standard error?

> and others report outcome as a continuous variable (% change in symptoms). For the latter, studies typically report the correlation coefficient.

Correlation between predictor and % change?

In general correlation and mean difference are measuring different 
things. Given two variables X and Y it is easy to construct an example 
where r(X, Y)=1 but |X - Y| is arbitrarily large.


  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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> 
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-- 
Michael
http://www.dewey.myzen.co.uk/home.html