[R] Regression and Sub-Groups Analysis in Metafor

[Dan Kolubinski]

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.  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
>>
>>       [[alternative HTML version deleted]]
>>
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>>
>>
> --
> Michael
> http://www.dewey.myzen.co.uk/home.html
>

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