[R-meta] metagen / low heterogeneity
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]]
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