[R-meta] MLMA - shared control group

Jorge Teixeira jorgemmtte|xe|r@ @end|ng |rom gm@||@com
Tue Aug 31 10:04:35 CEST 2021

Thanks Wolfgang and Reza - I have made some progress, at least.

Yes, I am thinking about 3-level MA.

Just 2 last points:

1) Is V** supposed to be equivalent to a certain default correlation value
in impute_covariance_matrix(). (IE. r=0.5)?

(** --> V
<- bldiag(lapply(split(dat, dat$study), calc.v))

The 2 methods seem to give different results, across multiple r values.

2) r values are pretty much based on "expert" opinion and faith? We don't
have tools to assess which value would be the best choice?


Viechtbauer, Wolfgang (SP) <wolfgang.viechtbauer using maastrichtuniversity.nl>
escreveu no dia segunda, 30/08/2021 à(s) 17:25:

> >-----Original Message-----
> >From: Reza Norouzian [mailto:rnorouzian using gmail.com]
> >Sent: Monday, 30 August, 2021 17:36
> >To: Viechtbauer, Wolfgang (SP)
> >Cc: Jorge Teixeira; R meta
> >Subject: Re: [R-meta] MLMA - shared control group
> >
> >Dear Wolfgang,
> >
> >Jorge may benefit from using cluster-robust estimates of its fixed
> >effects in his (perhaps 3-level) model. However, my current
> >understanding is that even assuming Cov(e_{ijk}, e_{ij'k}) = 0 for two
> >observed estimates from a person on say two outcomes in the same study
> >while that is, in reality, not the case (perhaps in a major way),
> >gives Type I error rates and confidence intervals' coverage that are
> >nearly accurate.
> Maybe or maybe not. You are essentially hoping that the bias in the
> variance components in this misspecified model compensates for the
> misspecification. That's does not sound like an approach I generally would
> rely on.
> Best,
> Wolfgang

	[[alternative HTML version deleted]]

More information about the R-sig-meta-analysis mailing list