Many thanks to both of you for the helpful responses to my post. The  
outcomes are all measured with the same units and I can indeed calculate  
the sampling variance from the 2 SDs I get from each study.
MP

Le , "Viechtbauer Wolfgang (STAT)"  
<wolfgang.viechtbauer@maastrichtuniversity.nl> a écrit :
> To add to Michael's response:



> There are several things you can do:



> 1) If the dependent variable is the same in each study, then you could  
> conduct the meta-analysis with the (raw) mean changes, ie, yi = m1i -  
> m2i, where m1i and m2i are the means at time 1 and 2, respectively. The  
> sampling variance of yi is vi = sdi^2 / ni, where sdi = sqrt(sd1i^2 +  
> sd2i^2 - 2*ri*sd1i*sd2i), sd1i and sd2i are the standard deviations of  
> the outcomes at time 1 and 2, respectively, ri is the correlation between  
> the outcomes at time 1 and time 2, and ni is the sample size. So, sdi is  
> the standard deviation of the change scores.



> When sdi is not reported, you will have to back-calculate sdi based on  
> what you have. You say that the p-value for the paired samples t-test is  
> reported. Typically, this will be a two-sided p-value, so ti = qt(pval/2,  
> df=ni-1, lower.tail=FALSE) will give you the value of the test statistic.



> And since ti = (m1i - m2i) * sqrt(ni) / sdi, you can back-calculate what  
> sdi is with sdi = (m1i - m2i) * sqrt(ni) / ti (you just have to make sure  
> that the sign of m1i - m2i and the sign of ti are matched up). And now,  
> you can even back-calculate what ri was by rearranging the equation for  
> sdi.



> 2) Often, the dependent variable is not the same in each study. Then you  
> will have to resort to a standardized outcome measure. There are two  
> options:



> a) standardization based on the change score standard deviation



> Then yi = (m1i - m2i) / sdi with sampling variance vi = 1/ni + yi^2 /  
> (2*ni).



> b) standardization based on the raw score standard deviation



> Then yi = (m1i - m2i) / sd1i with sampling variance vi = 2*(1-ri)/ni +  
> yi^2 / (2*ni).



> Note that we standardize based on sd1i (ie, the SD at time 1). So, we do  
> not pool sd1i and sd2i. Also, since ri is typically not reported, you  
> will have to use the method described above to back-calculate what ri was.



> Regardless of which approach you use, you can then proceed with the  
> meta-analysis using the yi and vi values. For example, with the metafor  
> package, if those values are in a data frame called dat,



> rma(yi, vi, data=dat)



> will fit a random-effects model.



> Those three outcome measures described above will actually be implemented  
> in an upcoming version of the metafor package. For now, you will have to  
> do the computations of yi and vi yourself.



> Best,



> Wolfgang



> --

> Wolfgang Viechtbauer, Ph.D., Statistician

> Department of Psychiatry and Psychology

> School for Mental Health and Neuroscience

> Faculty of Health, Medicine, and Life Sciences

> Maastricht University, PO Box 616 (VIJV1)

> 6200 MD Maastricht, The Netherlands

> +31 (43) 388-4170 | http://www.wvbauer.com





> > -----Original Message-----

> > From: r-help-bounces@r-project.org [mailto:r-help-bounces@r-project.org]

> > On Behalf Of Michael Dewey

> > Sent: Thursday, April 05, 2012 13:04

> > To: MP.Sylvestre@gmail.com; r-help@r-project.org

> > Subject: Re: [R] using metafor for meta-analysis of before-after studies

> >

> > At 18:39 04/04/2012, MP.Sylvestre@gmail.com wrote:

> > >Greetings,

> > >I wish to conduct a meta-analysis for which the outcome is a continuous

> > >variable measured on the same individuals before and after an

> > intervention.

> > >Hence, the comparison is not made between two groups, but within

> > >groups, at diffrent times.

> > >

> > >Each study reports the mean outcome and SD before the intervention and

> > >the mean outcome and SD after the intervention. While p-values for

> > >paired t-test (or similar methods for paired data) are reported in the

> > >studies, no estimate of the variability of the individual differences  
> are

> > available.

> >

> > If you know the p-value you can generate the t-value If you know the t-

> > value and the mean difference you can back calculate the standard errors

> > of the differences.

> >

> > Having said that I am not absolutely sure what the design of the primary

> > studies you are analysing is so my answer may not apply directly to your

> > problem.

> >

> >

> > >Can metafor deal with this sort of meta-analysis? I know that I can

> > >technically run metafor on these data, assuming that the groups are

> > >independent but my inference is likely to be wrong. On the other hand,

> > >I have no idea of the correlation within individuals.

> > >

> > >Thanks in advance,

> > >MP

> > >

> > > [[alternative HTML version deleted]]

> >

> > Michael Dewey

> > info@aghmed.fsnet.co.uk

> > http://www.aghmed.fsnet.co.uk/home.html

> >

> > ______________________________________________

> > R-help@r-project.org mailing list

> > https://stat.ethz.ch/mailman/listinfo/r-help

> > PLEASE do read the posting guide http://www.R-project.org/posting-

> > guide.html

> > and provide commented, minimal, self-contained, reproducible code.



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