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.Franchini at 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
>
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