Dear C?lia
I do not think the sensitivity analysis needs to be quite so complex as
you suggest. You can use the same imputed correlation for all your primary
studies. Then do it for (say) 0.2, 0.5, 0.8 and see what happens. If the
results are very different then use some intermediate values as well to see
where it all breaks down.
Michael
On 26/01/2018 22:50, C?lia Sofia Moreira wrote:
Hi!
I am studying a Pretest Posttest Control group design. I saw the
recommended method (Morris) to compute the effect sizes, presented in one
of the examples from Prof. Wolfgang? s webpage:
http://www.metafor-project.org/doku.php/analyses:morris2008
However, I don?t have pretest-posttest correlations. Prof. Wolfgang
suggests that in this case ?one can substitute approximate values (...)
and
conduct a sensitivity analysis to ensure that the conclusions from the
meta-analysis are unchanged when those correlations are varied?. However,
since I have many different outcomes, sensitive analysis will be a very
complex task. So, I was wondering if, instead of measure = "SMCR", I could
use measure ="SMD". More specifically:
datT <- escalc(measure="SMD", m1i=m_post, m2i=m_pre, sd1i=sd_post, sd2i=
sd_pre, n1i=N1, n2i=N2, vtype="UB" , data=datT)
datC <- escalc(measure="SMD", m1i=m_post, m2i=m_pre, sd1i=sd_post, sd2i=
sd_pre, n1i=N1, n2i=N2, vtype="UB" , data=datC)
dat <- data.frame(yi = datT$yi - datC$yi, vi = datT$vi + datC$vi)
If not, can you please explain the problem of this approach and inform
about the existence of any other simpler alternative?
Kind regards
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