[R] Regression and Sub-Groups Analysis in Metafor
bgunter.4567 at gmail.com
Tue May 31 22:43:35 CEST 2016
Briefly, as this is off-topic, and inline:
"The trouble with having an open mind is that people keep coming along
and sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
On Tue, May 31, 2016 at 11:32 AM, Dan Kolubinski <kolubind at lsbu.ac.uk> wrote:
> That makes perfect sense. Thank you, Michael. I take your point about not
> chasing the data and definitely see the risks involved in doing so. Our
> hypothesis was that the first, second and fourth variables would be
> significant, but the third one (intervention) would not be.
That is **not** a legitimate scientific hypothesis. Post to a
statistical list like stats.stackexchange.com to learn why not.
> double-check the dataset to make sure that there are not any errors and
> will report the results as we see them. I much appreciate you taking the
> Best wishes,
> On Tue, May 31, 2016 at 12:02 PM, Michael Dewey <lists at dewey.myzen.co.uk>
>> On 30/05/2016 19:27, Dan Kolubinski wrote:
>>> I am completing a meta-analysis on the effect of CBT on low self-esteem
>>> I could use some help regarding the regression feature in metafor. Based
>>> on the studies that I am using for the analysis, I identified 4 potential
>>> moderators that I want to explore:
>>> - Some of the studies that I am using used RCTs to compare an intervention
>>> with a waitlist and others used the pre-score as the control in a
>>> single-group design.
>>> - Some of the groups took place in one day and others took several weeks.
>>> - There are three discernible interventions being represented
>>> - The initial level of self-esteem varies
>>> Based on the above, I used this command to conduct a meta-analysis using
>>> standarized mean differences:
>>> MetaMod<-rma(m1i=m1, m2i=m2, sd1i=sd1, sd2i=sd2, n1i=n1, n2i=n2,
>>> mods=cbind(dur, rct, int, level),measure = "SMD")
>> You could also say mods = ~ dur + rct + int + level
>>> Would this be the best command to use for what I described? Also, what
>>> could I add to the command so that the forest plot shows a sub-group
>>> analysis using the 'dur' variable as a between-groups distinction?
>> You have to adjust the forest plot by hand and then use add.polygon to
>> add the summaries for each level of dur.
>>> Also, with respect to the moderators, this is what was delivered:
>>> Test of Moderators (coefficient(s) 2,3,4,5):
>>> QM(df = 4) = 8.7815, p-val = 0.0668
>>> Model Results:
>>> estimate se zval pval ci.lb ci.ub
>>> intrcpt 0.7005 0.6251 1.1207 0.2624 -0.5246 1.9256
>>> dur 0.5364 0.2411 2.2249 0.0261 0.0639 1.0090 *
>>> rct -0.3714 0.1951 -1.9035 0.0570 -0.7537 0.0110 .
>>> int 0.0730 0.1102 0.6628 0.5075 -0.1430 0.2890
>>> level -0.2819 0.2139 -1.3180 0.1875 -0.7010 0.1373
>>> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
>> So the totality of moderators did not reach an arbitrary level of
>>> From this, can I interpret that the variable 'dur' (duration of
>>> intervention) has a significant effect and the variable 'rct' (whether a
>>> study was an RCT or used pre-post scores) was just shy of being
>>> statistically significant? I mainly ask, because the QM-score has a
>>> p-value of 0.0668, which I thought would mean that none of the moderators
>>> would be significant. Would I be better off just listing one or two
>>> moderators instead of four?
>> At the moment you get an overall test of the moderators which you had a
>> scientific reason for using. If you start selecting based on the data
>> you run the risk of ending up with confidence intervals and significance
>> levels which do not have the meaning they are supposed to have.
>> Much appreciated,
>>> [[alternative HTML version deleted]]
>>> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
>>> PLEASE do read the posting guide
>>> and provide commented, minimal, self-contained, reproducible code.
> [[alternative HTML version deleted]]
> R-help at r-project.org mailing list -- To UNSUBSCRIBE and more, see
> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
> and provide commented, minimal, self-contained, reproducible code.
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