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.viechtba...@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-boun...@r-project.org [mailto:r-help-boun...@r-project.org] > > On Behalf Of Michael Dewey > > Sent: Thursday, April 05, 2012 13:04 > > To: mp.sylves...@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.sylves...@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 > > i...@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. [[alternative HTML version deleted]]
______________________________________________ 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.
