[R] Metafor - why use escalc?

[Viechtbauer Wolfgang (STAT)]

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-bounces at r-project.org [r-help-bounces at r-project.org] On Behalf Of Purssell, Ed [ed.purssell at kcl.ac.uk]
Sent: Friday, March 14, 2014 10:11 AM
To: r-help at 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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