Regression and Sub-Groups Analysis in Metafor

Briefly, as this is off-topic, and inline:
Bert Gunter

"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.

Cheers,
Bert



 I will

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
time!

Best wishes,
Dan

On Tue, May 31, 2016 at 12:02 PM, Michael Dewey <lists at dewey.myzen.co.uk

wrote:

In-line

On 30/05/2016 19:27, Dan Kolubinski wrote:

I am completing a meta-analysis on the effect of CBT on low self-esteem
and
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
significance.

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,

Dan

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--
Michael
http://www.dewey.myzen.co.uk/home.html

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