[R-meta] metagen / low heterogeneity

[Emerson Del Ponte]

Dear Sean,

I have been dealing with this kind of data and context (fungicide effect on
plant disease), and I think I know which is the previous paper you are
basing your analyses. Sorry for not replying to your specific questions,
but it seems there are primary aspects to look at before specificities of
the MA outcome.

I would recommend that you give a look at a number of works that followed
in the last decade (in case you didn't do it). I am quite sure that you are
treating several treatments from the same experiment as independent given
your high K - all treatments from the same trial are compared to a common
control. A network MA should be interesting to test. I've used the
arm-based approach in metafor (Wolfgang's help) and the contrast-based in
netmeta (Gerta's help).

Nothing wrong with Hedges G, but I would argue that log ratio is a more
directly interpretable effect size in our area - You really want to know
(as everybody in our field) the percent reduction in disease due to
fungicide use relative to the untreated check. The absolute or standardized
difference can be complicated if your control varies considerably among the
trials. Also, the criteria to classify and interpret Hedges G are not well
established in our field.

If you want to see examples specific for this kind of situation, I have
several codes on my github (check the link to the html report for each of
these):

https://github.com/emdelponte/paper-FHB-mixtures-meta-analysis
https://github.com/emdelponte/paper-fungicides-whitemold
https://github.com/emdelponte/paper-FHB-Brazil-meta-analysis

Hope this helps!

Emerson



Em seg., 11 de jan. de 2021 às 11:57, Sean <sean.toporek using gmail.com>
escreveu:

> Hello Meta-analysis Community,
>
> I've been using the metagen function in the meta package for a
> meta-analysis on fungicide efficacy to control a foliar pathogen in
> cucumbers. I'm using pre-calculated Hedge's G as my effect size and it's
> standard error. I'm not really a statistician, so I've been using this
> resource to hold my hand through the process (
> https://bookdown.org/MathiasHarrer/Doing_Meta_Analysis_in_R/random.html).
> I've run into a bit of a rut and I'm having a hard time getting help to
> interpret my results. I'm dealing with the issue of some of my dataset
> heterogeneity being nearly 0 (which could just be the case).
>
> *Here is an example of my output:*
>
> Number of studies combined: k = 288
>
>                                     SMD      95%-CI                t
> p-value
> Random effects model 0.3309 [ 0.2866; 0.3751] 14.72 < 0.0001
> Prediction interval                     [-0.2216; 0.8834]
>
> Quantifying heterogeneity:
>  tau^2 = 0.0783 [<0.0000; <0.0000]; tau = 0.2798 [<0.0000; <0.0000];
>  I^2 = 0.0% [0.0%; 0.0%]; H = 1.00 [1.00; 1.00]
>
> Test of heterogeneity:
>       Q     d.f.   p-value
>  165.46  287  1.0000
>
> *Here is the code:*
>
> metamkt <- metagen(G,
>                     seG,
>                     data = mkt,
>                     studlab = paste(Study),
>                     comb.fixed = FALSE,
>                     comb.random = TRUE,
>                     method.tau = "SJ",
>                     hakn = TRUE,
>                     prediction = TRUE,
>                     sm = "SMD")
>
> My first red flag is of course "I^2 = 0.0%", then that my Q p-value is 1.
> The interpretation being that the observed heterogeneity is completely
> random. I have a couple datasets, with the highest I^2 = 17.4%. The reason
> I find it odd, is that when I do subgroup analysis (even though I'm not
> supposed to with such low / non-existat heterogeneity), the results make
> biological sense. My data spans the last decade and the results are also
> similar with a meta-analysis done in the previous decade on the same topic.
> This makes me feel like I've made some sort of error at some point in my
> workflow and I was wondering if you have any diagnostic recommendations for
> me? One thing that worries me is that my standard errors for my Hedge's G
> values are so similar since all treatments in each study have 4
> replications, but maybe it shouldn't.
>
> Best,
>
> Sean
>
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>
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>


-- 
*Emerson M. Del Ponte*
Universidade Federal de Viçosa, Brazil
Chair of the Graduate Studies <http://www.dfp.ufv.br/graduate/> in Plant
Pathology
EIC for Tropical Plant Pathology <http://sbfitopatologia.org.br/tpp/>
Co-Founder of Open Plant Pathology <https://www.openplantpathology.org/>
My websites: Twitter <https://twitter.com/edelponte> | GitHub
<https://github.com/emdelponte> | Google Scholar
<https://scholar.google.com.br/citations?user=a1rPnI0AAAAJ> | ResearchGate
<https://www.researchgate.net/profile/Emerson_Del_Ponte>
Tel +55 31 36124830

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