[R-meta] metagen / low heterogeneity

[Sean]

I apologize for the formatting. Here is the ouput and code again
below. I think this should be more readable now that I've selected
plain text.

Michael, well that is good news. If I did have high heterogeneity and
hadn't planned to use a moderator, does that just mean I should
consider looking for one? Whereas in my case, I knew what I was
interested in, so my heterogeneity does not need to be considered as a
prerequisite?

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")

Sean



On Mon, Jan 11, 2021 at 11:11 AM Michael Dewey <lists using dewey.myzen.co.uk> wrote:
>
> Dear Sean
>
> Some comments in-line. It is difficult to read your output because you
> posted in HTML so I will leave that to people more familiar with the
> software. Next time it would help to set your mailer to use plain text
> so your message does not get mangled.
>
> On 11/01/2021 14:56, Sean wrote:
> > 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]
>
> The fact that your prediction interval is so much wider than the
> confidence interval does suggest there is heterogeneity here.
> >
> > 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.
>
> No, no, a thousand times no. You use a moderator if there is a
> scientific hypothesis which justifies it not because of observed
> heterogeneity. In this case if there is a biological theory behind a
> moderator then use it.
>
> Michael
>
>   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
> >
> >       [[alternative HTML version deleted]]
> >
> > _______________________________________________
> > R-sig-meta-analysis mailing list
> > R-sig-meta-analysis using r-project.org
> > https://stat.ethz.ch/mailman/listinfo/r-sig-meta-analysis
> >
>
> --
> Michael
> http://www.dewey.myzen.co.uk/home.html