[R] metafor and meta-analysis at arm-level

[Viechtbauer Wolfgang (STAT)]

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-bounces at r-project.org
[mailto:r-help-bounces at r-project.org] On Behalf Of Angelo Franchini
Sent: Tuesday, July 06, 2010 10:37 To: Wolfgang Viechtbauer
Cc: r-help at 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.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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