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

[Célia Sofia Moreira]

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>:

> 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
>

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