[R-meta] metagen / low heterogeneity

[Sean]

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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