[R-meta] metagen / low heterogeneity
Michael Dewey
||@t@ @end|ng |rom dewey@myzen@co@uk
Tue Jan 12 14:21:52 CET 2021
On 11/01/2021 16:45, Sean wrote:
> 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?
The crucial thing is the scientific context. I do not work in the same
area as you so my examples are from my field, not yours, but I hope are
helpful.
If the primary studies were all very similar then you would not expect
heterogeneity and you might be prompted to look for explanations for
even mild amounts. For instance if all the primary studies had studied
the same dose of drug in people with very tightly defined illness in
countries with very similar health care symptoms then any heterogeneity
might lead you, post hoc, to find out why.
If on the contrary the studies had examined a complex health care
systems intervention in countries across the globe in patients who might
vary considerably then you would be very surprised not to see
heterogeneity. In that case you would be less inclined to look for
explanations.
If you had a theory that outcomes were related to some other variable
then you might use that as a moderator irrespective of the amount of
heterogeneity. For instance in a study of a skills-based therapy you
might have a theory that outcomes are different now from what they used
to be so you would find it worth while looking at that whatever. For
instance is centres in each study have been doing a particular operation
for different amounts of time do the ones who have been doing it for
longest have have better or worse outcomes.
Michael
>
> 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
>
--
Michael
http://www.dewey.myzen.co.uk/home.html
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