[R-meta] Accounting for pre-treatment data

[Rachel Schwartz]

Hello all- I am conducting a small meta-analysis that compares 3 treatments
against 3 independent controls. I believe the standard procedure is to
calculate effect sizes (Hedges' g) that compare only post-treatment data,
given the assumption that randomization should equalize pre-treatment data
across groups. However, my 3 studies have small samples (*n*'s 19-63) --
because randomization can fail with small samples, I don't feel comfortable
assuming that pre-treatment data are comparable across groups. To account
for this, I was thinking of calculating two change scores ("SMCC"): one
within the treatment arms (pre-post) and another within the control arms
(pre-post)... then subtracting the resulting effect sizes, as in the
following example:
http://r.789695.n4.nabble.com/metafor-standardized-mean-difference-in-pre-post-design-studies-td4705800.html


Is this approach advisable, or should I just go with post data only?
Perhaps there's some other way to control for pre data without resorting to
change scores? The comprehensive meta-analysis software seems to have some
way of accounting for pre-treatment data. Is there a way to approach the
question similarly in metafor?

Finally, if I should mimic the example above, is it correct to simply add
the variances as this poster did? (datFin <- data.frame(yi = datE$yi -
datC$yi, *vi = datE$vi + datC$vi*)

Many thanks to anyone who has advice.

-- 
Rachel A. Schwartz, M.A.
Ph.D. Student | University of Pennsylvania
425 S. University Avenue
Philadelphia, PA 19104

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