Often, there is a mix of information available from the various studies that
needs to be used to compute the effect sizes or outcomes to be used for the
meta-analysis. Then you have to 'build up' your dataset in multiple steps and
you cannot bypass first using escalc().
As a very basic example, suppose you have 2x2 table data for most studies, but
for a few studies, you only have the odds ratio and corresponding 95% CI (since
this is all that the authors reported). The odds ratios are easily converted
into log odds ratios and the CIs can be used to obtain the sampling variances
of the log odds ratios. And for the studies for which the 2x2 table data is
available, one can use escalc() to compute the log odds ratios and
corresponding sampling variances.
Best,
Wolfgang
________________________________________
From: r-help-boun...@r-project.org [r-help-boun...@r-project.org] On Behalf Of
Purssell, Ed [ed.purss...@kcl.ac.uk]
Sent: Friday, March 14, 2014 10:11 AM
To: r-help@r-project.org
Subject: [R] Metafor - why use escalc?
Dear All
As you can specify the data directly to rma.uni via n1i, m1i, sd1i, etc in
Metafor, why would you ever want to use escalc to calculate yi and vi? Aren't
these just intermediate steps to the final pooled effect size which is
calculated by rma.uni; or is there some advantage to calculating yi and vi
separately using escalc?
Thanks
Ed
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