[R-meta] Using control-only and treatment-only studies in 'metafor' -- can you calculate effect sizes with NAs in your table?

Veronica Frans verofr@n@ @ending from gm@il@com
Tue Oct 30 20:38:20 CET 2018

Hello, Wolfgang,

Thanks for your reply.

The 'mean accuracy score' I am referring to here is the Area Under the
Receiver Operating Curve (a plot of true positive and false positive rates
of a model prediction), and is calculated from testing a trained model's
ability to accurately determine the presence of absence of an occurrence in
a given spatial grid. Values below 0.5 indicate complete randomness, and
values closer to 1 imply the highest predictability. It is one of the
standard measures of species distribution modeling in ecology, and I am
using this score to test models that follow a newer procedure (the
treatment) versus models that don't (the control). The goal is to see if
the treatment has a greater effect on accuracy than the control.

I am obviously new to meta-analyses, so any suggestions are definitely
appreciated. Thanks again for your help!


On Tue, Oct 30, 2018 at 2:40 PM Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:

> Dear Veronica,
> Measure 'SMD' is for computing the standardized mean difference between
> two groups. It does not seem applicable to your data.
> Can you describe in a bit more detail what these "mean accuracy scores"
> are? How are they computed within an individual study?
> Best,
> Wolfgang
> -----Original Message-----
> From: R-sig-meta-analysis [mailto:
> r-sig-meta-analysis-bounces using r-project.org] On Behalf Of Veronica Frans
> Sent: Tuesday, 30 October, 2018 18:29
> To: r-sig-meta-analysis using r-project.org
> Subject: [R-meta] Using control-only and treatment-only studies in
> 'metafor' -- can you calculate effect sizes with NAs in your table?
> Dear forum,
> I would like to use the 'metafor' package for my meta-analysis. I am
> comparing the results (mean accuracy score from 0  to 1) of articles that
> use one procedure (the 'treatment'; group 1) versus those that don't (the
> 'control'; group 2). However, all of my studies present results for only
> the treatment or only the control, but never both.
> To run the escalc() function (measure=SMD), is it possible to have studies
> with NA's in m1i, sd1i, n1i, and vice-versa?
> Unfortunately, when I use my table in R, the escalc() function gives me
> NA's for yi and vi.
> Here's an example of the code I used:
> mod.means <-data.frame(
>                 study = c("UID6","UID7","UID11","UID13","UID17","UID18"),
>                 n1i = c(1,1,16,NA,NA,21), #number in treatment
>                 n2i = c(NA,NA,NA,2,2,NA), #number in control
>                 m1i = c(.931,.81,.977,NA,NA,.878), #treatment means
>                 m2i = c(NA,NA,NA,.865,.69,NA),     #control means
>                 sd1i = c(0,0,.012,NA,NA,.0386),  #treatment sd
>                 sd2i = c(NA,NA,NA,.05,.03,NA),   #control sd
>                 scale = c(3,4,1,1,3,2)           #potential moderator
>                 )
> all.meta <- escalc(measure = "SMD",
>                    m1i = m1i, m2i=m2i,     #means
>                    sd1i=sd1i, sd2i = sd2i,  #standard deviation
>                    n1i=n1i, n2i = n2i,         #numbers
>                    data = mod.means)
> all.meta  #show table
> Perhaps I should format my table in a different way or consider a different
> meta-analysis approach other than "SMD"?
> Any advice on this is greatly appreciated. Thank you for your time!
> Sincerely,
> Veronica Frans

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