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

Dear Prof. Michael Dewey,

Thank you very much for your encouraging comments. Indeed, I considered 
different values for the correlation and the results on the differences 
(between the two standardised mean changes values - "SMCR"), for each 
outcome, were the same. Only the variances of these differences varied a 
bit, according to the following rule: higher correlation --> lower 
variance. Thus, following your advice, maybe r=.5 is a reasonable 
choice. Do you agree?

Kind regards,
 ?celia

2018-01-27 13:54 GMT+00:00 Michael Dewey <lists at dewey.myzen.co.uk 
<mailto:lists at dewey.myzen.co.uk>>:

    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
        <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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