[R-meta] Effect sizes calculation in Pretest Posttest Control design

[Michael Dewey]

Dear Célia

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, Célia 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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> 
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-- 
Michael
http://www.dewey.myzen.co.uk/home.html