[R-meta] escale ROM or SMD

James Pustejovsky jepu@to @end|ng |rom gm@||@com
Mon Jul 12 16:10:42 CEST 2021

Hi Lukas,

I think there are (at least) four relevant considerations here:
1) Measurement properties: what is proline concentration? Is it measured on
a ratio scale, such that ROM is a sensible way to describe change (over
time or as a result of intervention)? If you provide a bit more detail
about what the outcome is (for us social scientists on the listserv), then
perhaps others will be able to weigh in also.
2) Theory: Is there any relevant botanical theory that would indicate how
drought stress should be related to proline concentration?
3) Empirical features: It can be helpful to create scatterplots showing the
relationship between the M and the SD of the outcome in each group and
between the Ms in different comparison groups. If the outcomes are on
drastically different scales, then the plots can be created within
subgroups that use the same or similar measurement instruments. If the SD
of the outcome is strongly related to the M, then I would take this as an
indication that ROM might be more appropriate than SMD.
4) Heterogeneity: Which model has less unexplained variability (as measured
by I^2, for instance)? All else equal, I would prefer the effect size
metric where the meta-analytic model has greater explanatory power. From
what you've said, it sounds like the ROM would win out here due to fewer
outliers and no apparent funnel plot asymmetry.

I've listed these considerations in what I would consider to be decreasing
order of priority (1st being essential, 2nd being important, 3rd and 4th
being matters of judgement). Others might have different perspectives,

Kind Regards,

On Mon, Jul 12, 2021 at 5:43 AM Lukas Dylewski <dylewski91 using gmail.com> wrote:

> Dear All,
> I conducted a mixed meta-regression model to check the effect of drought
> stress on proline concentration in leaves. In the model, I include the
> following moderators: duration of drought (in the day), seed mass, and
> plant type (coniferous vs. deciduous). The duration of the drought affects
> the proline content, therefore it was included in the model.
> When I calculate Hedge g (measure=SMD) values I got very big values for
> some records (e.g. 100, 50, etc.) and publication bias. However, when I
> calculate effect size using ROM I got very nice numbers without publication
> bias.
> My first question: Can I use the ROM method for my dataset (I attach the
> database: proline) or should I use the SMD?
> This is my code (with ROM measure)
> z<-proline
> z$logmass<-log(z$seed.mass.mg+1)
> hedges<-escalc(measure="ROM",data=z,append=T,m1i=experiment,n1i=n.experiment,sd1i=sd.experiment,m2i=control,n2i=n.control,sd2i=sd.control)
> hedges
> res1 <- rma.mv(yi, vi, mods = ~ logmass + I(logmass^2)+
> factor(plant.type) + drougth.day + I(drougth.day^2),
> random=~1|references,data=hedges, method="REML")
> After the model summary, I would like to calculate the mean effect size
> for the plant type (coniferous and deciduous separately). Does this code
> correct?
> m <- mean(hedges$logmass)
> n <- mean(hedges$drougth.day)
> For conifeorus:
> predict(res1, newmods = c(m, m^2, 0,n,n^2))
> For deciduous:
> predict(res1, newmods = c(m, m^2, 1,n,n^2))
> --
> Łukasz Dylewski, PhD.
> Institute of Dendrology,
> Polish Academy of Sciences,
> Parkowa 5, 62-035 Kórnik, Poland
> _______________________________________________
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> R-sig-meta-analysis using r-project.org
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