[R-meta] Multivariate meta-analysis when "some studies" are multi-outcome
Reza Norouzian
rnorouz|@n @end|ng |rom gm@||@com
Wed Mar 17 17:45:10 CET 2021
Dear Gladys,
In addition to Wolfgang's excellent explanation, you may want to check:
https://stats.stackexchange.com/a/228814/140365
Best,
Reza
On Wed, Mar 17, 2021 at 7:34 AM Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:
> Dear Gladys,
>
> Whether this makes sense depends on how thse variables are coded. There
> have been several posts in the past on this mailing list where this was
> discussed. One that I quickly found is:
>
> https://stat.ethz.ch/pipermail/r-sig-meta-analysis/2018-July/000896.html
>
> Best,
> Wolfgang
>
> >-----Original Message-----
> >From: Gladys Barragan-Jason [mailto:gladou86 using gmail.com]
> >Sent: Tuesday, 16 March, 2021 11:39
> >To: Viechtbauer, Wolfgang (SP)
> >Cc: Simon Harmel; R meta
> >Subject: Re: [R-meta] Multivariate meta-analysis when "some studies" are
> multi-
> >outcome
> >
> >Dear Wolfgang,
> >
> >Following Simon's question, I am also comparing the efficiency of
> programs (pre-
> >post comparisons).
> >For some of them, I do have several effect sizes for one study and one
> lab. So I
> >was using the following code to account for it.
> >
> >res.ExpNC<-rma.mv(yi, vi, mods= ~ categ , random=list(
> ~1|study,~1|lab),data=dat2)
> >
> >But I am now wondering whether I should do this instead:
> >
> >dat2$estid <- 1:nrow(dat2)
> >res.ExpNC<-rma.mv(yi, vi, mods= ~ categ , random=list(
> >~1|study/estid,~1|lab/estid),data=dat2)
> >
> >What do you think?
> >
> >Thanks a lot for your response,
> >
> >Gladys
> >
> >Le mar. 16 mars 2021 à 11:28, Viechtbauer, Wolfgang (SP)
> ><wolfgang.viechtbauer using maastrichtuniversity.nl> a écrit :
> >Dear Simon,
> >
> >At the very least, you should add random effects at the level of the
> studies and
> >at the level of the estimates, so:
> >
> >dat$estid <- 1:nrow(dat)
> >
> >and then
> >
> >random = ~ 1 | id / estid
> >
> >For longitudinal data, one could also consider using some kind of
> autocorrelation
> >structure for the estimates within studies. There are some examples here:
> >
> >https://wviechtb.github.io/metafor/reference/dat.ishak2007.html
> >https://wviechtb.github.io/metafor/reference/dat.fine1993.html
> >
> >clubSandwich::impute_covariance_matrix() also allows for the construction
> of a V
> >matrix with an autocorrelation structure.
> >
> >If the different outcomes are meaningfully related across studies (i.e.,
> outcome
> >'1' stands for the same thing across all studies), then one could also
> consider
> >using an unstructured var-cov matrix with correlated random effects for
> outcomes
> >within studies. This would be akin to:
> >
> >https://www.metafor-project.org/doku.php/analyses:berkey1998
> >
> >Best,
> >Wolfgang
> >
> >>-----Original Message-----
> >>From: Simon Harmel [mailto:sim.harmel using gmail.com]
> >>Sent: Monday, 15 March, 2021 17:31
> >>To: Viechtbauer, Wolfgang (SP)
> >>Cc: R meta
> >>Subject: Re: [R-meta] Multivariate meta-analysis when "some studies" are
> multi-
> >>outcome
> >>
> >>Dear Prof. Viechtbauer,
> >>
> >>Many thanks for your response. I found the following particularly helpful
> >>(
> https://stat.ethz.ch/pipermail/r-sig-meta-analysis/2019-March/001484.html
> ).
> >>
> >>So, I went from my initial model: `rma.mv(d, V = SE^2, mods =
> ~factor(outcome)-1,
> >>random= ~1|id, data = dat)`
> >>to now:
> >>
> >>`V <- clubSandwich::impute_covariance_matrix(vi = dat$SE^2, cluster =
> dat$id, r =
> >>0.7)`
> >>`rma.mv(d, V = V, mods = ~factor(outcome)-1, random= ~1|id, data = dat)`
> >>
> >>However, what type of dependence is accounted for by the multilevel part
> (i.e.,
> >>`random= ~1|id`), and what type of dependence is accounted for by
> including the
> >>imputed variance-covariance matrix?
> >>
> >>Specifically, in my data, all primary studies (n=52) are longitudinal,
> 15 of them
> >>are multi-outcome, and almost all are multi-group treatments. Are all of
> these
> >>types of dependence reasonably accounted for?
> >>
> >>Many thanks for your consideration,
> >>Simon
> >>
> >>On Mon, Mar 15, 2021 at 6:54 AM Viechtbauer, Wolfgang (SP)
> >><wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:
> >>Hi Simon,
> >>
> >>I would suggest to search/browse the archives, as this kind of question
> has been
> >>discussed at various points in the past. The archives can be found here:
> >>
> >>https://stat.ethz.ch/pipermail/r-sig-meta-analysis/
> >>
> >>There is no built-in search functionality for the archives, but one can
> restrict
> >>search engines to conduct searches at particular sites. For example, if
> you do a
> >>google search including
> >>
> >>site:https://stat.ethz.ch/pipermail/r-sig-meta-analysis/
> >>
> >>you should only get 'hits' from the mailing list archives. The same
> should work
> >>with DuckDuckGo. Note sure about other engines.
> >>
> >>Note that search engines index the archives at semi-regular intervals,
> so the
> >most
> >>recent posts will not show up this way, but those can be searched
> manually.
> >>
> >>Best,
> >>Wolfgang
> >>
> >>>-----Original Message-----
> >>>From: R-sig-meta-analysis [mailto:
> r-sig-meta-analysis-bounces using r-project.org] On
> >>>Behalf Of Simon Harmel
> >>>Sent: Saturday, 13 March, 2021 23:53
> >>>To: R meta
> >>>Subject: [R-meta] Multivariate meta-analysis when "some studies" are
> multi-
> >>outcome
> >>>
> >>>Dear All,
> >>>
> >>>I'm conducting a meta-analysis where 15 out of 52 studies have used more
> >>>than one outcome variable. In addition, almost all studies include
> multiple
> >>>treatments.
> >>>
> >>>A shortened version (i.e., without moderators) of our dataset appears
> below
> >>>(`*id`=study id; `d`=effect size; `SE` = standard error;
> `outcome`=outcome
> >>>variable index*).
> >>>
> >>>I was wondering what would be the appropriate modeling options for such
> a
> >>>situation?
> >>>
> >>>I appreciate your expertise and consideration,
> >>>Simon
> >>>
> >>>*#-- R data and code:*
> >>>dat <- read.csv("
> https://raw.githubusercontent.com/hkil/m/master/tst.csv")
> >>>
> >>>library(metafor)
> >>>rma.mv(d, V = SE^2, mods = ~factor(outcome)-1, random= ~1|id, data =
> dat)
> >>>## I'm assuming this would be an insufficient model
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