[R-meta] Pooling studies with binary and continuous outcomes
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 at kcl.ac.uk<mailto:graham.blackman at kcl.ac.uk> Telephone: 02078485228 [[alternative HTML version deleted]]
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