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

[Viechtbauer Wolfgang (STAT)]

Dear Angelo,

This is (currently) not possible.

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: Angelo Franchini [mailto:Angelo.Franchini at bristol.ac.uk]
Sent: Sunday, August 08, 2010 13:09
To: Viechtbauer Wolfgang (STAT)
Cc: r-help at r-project.org
Subject: RE: [R] metafor and meta-analysis at arm-level

> Dear Wolfgang,
>
> Is there any way for rma to add random effects only to each treatment
> arm, but not to the control one?
>
> Many thanks,
> Angelo
>
>
> On Thu, August 5, 2010 6:21 pm, Viechtbauer Wolfgang (STAT) wrote:
>> Dear Angelo,
>>
>> rma(yi=o, sei=se, mods=~s+t-1, method="REML")
>>
>> is *a* way to run the arm-based pairwise meta-analysis. Whether it is the
>> *correct* way is a question I cannot answer.
>>
>> lme(o~s+t-1, random=~t-1 | s, weights=(~ se^2))
>>
>> is a different model. First of all, it adds a random effect only to
>> each treatment arm within each study, while the rma model above gives
>> a random effect to each observation. Moreover, the lme model assumes
>> that the sampling variances are only known up to a proportionality
>> constant, while the rma model assumes that they are known exactly.
>>
>> Similarly,
>>
>> lm(formula = o ~ s + t - 1, weights = 1/se.o^2)
>>
>> assumes that the sampling variances are only known up to a
>> proportionality constant, while rma (with method="FE") assumes that they
>> are known exactly.
>>
>> For the same reason will
>>
>> rma(yi=e, sei=se, method="REML")
>> lme(e~1, random=~1 | s, weights=(~ se.e^2))
>>
>> and
>>
>> rma(yi=e, sei=se.e, method="FE")
>> lm(e~1, weights = 1/se.e^2)
>>
>> not give you the same results.
>>
>> 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: Angelo Franchini [mailto:Angelo.Franchini at bristol.ac.uk]
>> Sent: Wednesday, August 04, 2010 16:26
>> To: Viechtbauer Wolfgang (STAT)
>> Cc: 'Angelo Franchini'; r-help at r-project.org
>> Subject: RE: [R] metafor and meta-analysis at arm-level
>>
>>> Hello Wolfgang.
>>>
>>> I'd appreciate if you could help me check whether I am doing the
>>> proper thing to do an arm-level meta-analysis with metafor and what
>>> differences there might be in trying to do the same with lme and lm.
>>>
>>> I am following the arm based model described in section 3.2 of the
>>> Salanti's paper that you mentioned in your previous e-mail, namely:
>>>
>>> theta = B*eta + X*mu + W*beta
>>>
>>> where:
>>> theta = vector of parameter for outcomes in treatment arms (theta_ij
>>> for study i, treat. arm j) eta    = vector of parameter for outcomes in
>>> control arms (eta_i for study i) mu     = vector of effects (treat. vs
>>> cont.) (mu_ij for study i, treat. arm j) beta   = vector of random
>>> effects (beta_ij for study i, treat. arm j)
>>>
>>>
>>> In my specific case with a pairwise meta-analysis, I had my data
>>> arranged as in columns for the following variables: s t o se
>>>
>>> with
>>> s as study/trial identifier
>>> t as 0/1 for control/treatment arm
>>> o as observed outcome in control or treatment arm
>>> se as standard error of that outcome measure
>>>
>>> I then ran metafor as:
>>> rma(yi=o, sei=se, mods=~s+t-1, method="REML")
>>>
>>> for random effects, and REML replaced by FE for fixed effects.
>>>
>>> Is that the correct way to run the arm-based pairwise meta-analysis?
>>>
>>> Shouldn't I be able to obtain similar results with LME for
>>> random-effects by using the command: lme(o~s+t-1, random=~t-1 | s,
>>> weights=(~ se^2))
>>>
>>> and for fixed-effects with:
>>> lm(formula = o ~ s + t - 1, weights = 1/se.o^2)
>>>
>>>
>>> For the trial-based pairwise meta-analysis I used:
>>> data arranged as:
>>> s e se
>>>
>>> with:
>>> s study
>>> e effect
>>> se standard error
>>>
>>> and commands:
>>> rma(yi=e, sei=se, method="REML")
>>>
>>> or
>>>
>>> lme(e~1, random=~1 | s, weights=(~ se.e^2))
>>>
>>> for random-effects, while for fixed-effects:
>>> rma(yi=e, sei=se.e, method="FE")
>>> lm(e~1, weights = 1/se.e^2)
>>>
>>> Does that make sense?
>>>
>>>
>>> Many thanks for any comment/advice on this matter.
>>> Best regards,
>>> Angelo