With "appropriate design matrix", I mean the X matrix in the mixed-effects model y = Xb + u + e, where y is the vector of outcomes, u is a vector of (possibly correlated) random effects, and e is a vector of (possibly) random errors. The X matrix is specified via the 'mods' argument in the rma() function. If y consists of arm-level outcomes, then you need appropriate dummy variables in X to code what type of arm the outcome corresponds to.
Have you read, for example: Salanti, G., Higgins, J. P. T., Ades, A. E., & Ioannidis, J. P. A. (2008). Evaluation of networks of randomized trials. Statistical Methods in Medical Research, 17(3), 279-301. This article may be helpful. Best, -- Wolfgang Viechtbauer http://www.wvbauer.com/ Department of Methodology and Statistics Tel: +31 (0)43 388-2277 School for Public Health and Primary Care Office Location: Maastricht University, P.O. Box 616 Room B2.01 (second floor) 6200 MD Maastricht, The Netherlands Debyeplein 1 (Randwyck) ----Original Message---- From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On Behalf Of Angelo Franchini Sent: Tuesday, July 06, 2010 10:37 To: Wolfgang Viechtbauer Cc: r-help@r-project.org; Angelo Franchini Subject: Re: [R] metafor and meta-analysis at arm-level > Hello Wolfgang, > > Thank you very much for your response. > When you mentionthe "appropriate design matrix", do you mean by that > the 'n1i, n2i, m1i, m2i, sd1i, sd2i' arguments of the rma function, > or am I missing something? I read the documentation on metafor > (introduction), rma/rma.uni and escalc, and that was the only way > that I could find for the package to use information at the arm-level > rather than the trial one. > > As for the complexity of possible correlations between effects, that > is something to be considered for the network analysis case, correct? > > Many thanks. > > Best regards, > Angelo > > > > On Sun, July 4, 2010 6:06 am, Wolfgang Viechtbauer wrote: >> Hello Angelo, >> >> You can either supply the arm-level outcomes and corresponding >> sampling variances directly (via the yi and vi arguments) or supply >> the necessary information so that the escalc() or rma() functions can >> calculate an appropriate arm-level outcome (such as the log odds). >> See the documentation of the escalc() function and in particular the >> part about proportions and tranaformations thereof as possible >> outcome measures. >> >> This is the easy part. Then you need to set up an appropriate design >> matrix to code what arm each observed outcome corresponds to. And >> finally comes the tricky/problematic part. The rma() function assumes >> independent sampling errors and independent random effects for each >> observed outcome. Independent sampling errors is (usually) ok when >> using arm-level outcomes, but the independent random errors part may >> not be appropriate. This is why I am working on functions that do not >> make this independence assumption. With those functions, you can then >> carry out multivariate and network-type meta-analyses. These >> functions will become part of the metafor package in the future. >> >> Best, >> >> -- >> Wolfgang Viechtbauer >> http://www.wvbauer.com >> >> "Angelo Franchini" <angelo.franch...@bristol.ac.uk> wrote: >> >>> Hi, >>> >>> I have been looking for an R package which allowed to do >>> meta-analysis (both pairwise and network/mixed-treatment) at >>> arm-level rather than at trial-level, the latter being the common >>> way in which meta-analysis is done. By arm-level meta-analysis I >>> mean one that accounts for data provided at the level of the >>> individual arms of each trial and that does not simply derive the >>> difference between arms and do the meta-analysis on that. >>> >>> I am not sure metafor can do that, but hopefully someone more >>> experienced on it can clarify that to me. I can see that it can take >>> data in both forms, arm and trial level, but it looks as if the >>> arm-level information would be converted into trial one through >>> escalc and the latter then used for the meta-analysis. Is that >>> right? >>> >>> Many thanks. >>> >>> Angelo >>> >>> >>> -- >>> NIHR Research Methods Training Fellow, >>> Department of Community Based Medicine >>> University of Bristol >>> 25 Belgrave Road >>> Bristol BS8 2AA >>> >>> Tel. 0779 265-6552 >>> >>> ______________________________________________ >>> 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. ______________________________________________ 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.
