[R-meta] Violation in non-independece of errors (head to head studies and mutlilevel meta-analysis)?

[Viechtbauer Wolfgang (SP)]

Dear Natan,

If you reuse the information from a placebo group to compute multiple effects (i.e., treatment 1 vs placebo, treatment 2 vs placebo, etc.), then this automatically induces dependency in the sampling errors of the estimates. Code to compute the covariance for various effect size measures can be found here:

http://www.metafor-project.org/doku.php/analyses:gleser2009

So, you need to construct the full V matrix, use rma.mv(), and also include appropriate random effects (at least for studies and for each row of the dataset) in the model. So, something like this:

dat$id <- 1:nrow(dat)
res <- rma.mv(yi, V, mods = ~ <whatever fixed effects you think are needed>, 
              random = ~ 1 | study/id, data=dat)

I am a bit confused about:

>We are trying to avoid network meta-analysis, given we want our results
>to be adjusted by several moderators that affect antidepressant response.

Why do you think that network meta-analysis is not compatible with 'adjustment by moderators'?

Best,
Wolfgang

>-----Original Message-----
>From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-
>project.org] On Behalf Of Natan Gosmann
>Sent: Saturday, 03 March, 2018 20:46
>To: r-sig-meta-analysis at r-project.org
>Subject: [R-meta] Violation in non-independece of errors (head to head
>studies and mutlilevel meta-analysis)?
>
>Hello all,
>
>We are conducting a large multilevel meta-analysis using the metafor
>package considering all RCTs that assessed medications vs placebo for
>psychiatric disorders.
>
>We included all available outcomes from each study and therefore, we are
>considering study and assessment instrument (scale) as random variables
>in
>the model. The yi comes from differences in standardized mean change
>between medication and placebo for each study.
>
>We are trying to avoid network meta-analysis, given we want our results
>to
>be adjusted by several moderators that affect antidepressant response.
>
>However, we have doubts about how we can handle head to head studies
>(studies with more than one medication) and studies with distinct dosages
>of the same medication. We were thinking to just calculate differences
>from
>placebo (but placebo would be the same group for those studies - would be
>the contrast group for more then one medication or dosage group).
>Including
>study ID as random variable already accounts for violation in
>non-independence of errors? Is that an appropriate way of doing that?
>
>Alternatively, should we select only one medication from head to head
>trials?
>
>I would very much appreciate if you could help us with that.
>
>Best regards,
>Natan