[R-meta] Proportional meta-analysis

Michael Dewey lists at dewey.myzen.co.uk
Fri Feb 16 12:54:45 CET 2018

Dear Ahmed

They are indeed of relevance as they represent the estimated effect of 
each material. So Light-polymerised sealant is on average 0.25 higher 
than your reference category with confidence interval between 0.07 and 
0.43. And so on.

On 15/02/2018 18:50, Ahmed Tarek wrote:
> Dear Wolfgang,
> Thank you very much for your reply. I did the subgroup meta-analysis, 
> and it did indeed show me the relationship between the material 
> (intercept) and the other materials. I then simply stated that the 
> pooled estimates previously calculated where signifiant or not when 
> compared to the intercept.
> Should I also report the numbers in the first column? “estimate”. Are 
> they of releveance? because I don’t seem to know how to interpret them.
> Regards,
> Ahmed Bedir
>> On Feb 12, 2018, at 6:49 PM, Viechtbauer Wolfgang (SP) 
>> <wolfgang.viechtbauer at maastrichtuniversity.nl 
>> <mailto:wolfgang.viechtbauer at maastrichtuniversity.nl>> wrote:
>> Dear Ahmed,
>> See my comment below.
>>> -----Original Message-----
>>> From: R-sig-meta-analysis [mailto:r-sig-meta-analysis-bounces at r-
>>> project.org <http://project.org>] On Behalf Of Ahmed Tarek
>>> Sent: Friday, 09 February, 2018 20:46
>>> To: r-sig-meta-analysis at r-project.org 
>>> <mailto:r-sig-meta-analysis at r-project.org>
>>> Subject: [R-meta] Proportional meta-analysis
>>> Dear All,
>>> In a previous email I have been explaining about a proportional meta
>>> analysis I have been performing. I am working on comparing the retention
>>> rates of different materials. I first took the approach of grouping
>>> different materials with similar follow up times , and calculating a
>>> summary proportion of all materials, however I later found out that
>>> performing a moderator analysis ( in this case “materials”) would only
>>> compare the materials against the summary proportion (?) , which is not
>>> my aim. I aim to see how each material performed, and compare directly
>>> between them. For example, I have materials A,B,C,D and I wish to compare
>>> the retention between A and B, to see if they are significantly
>>> different.
>> But this is exactly what a moderator analysis will do. Fit a single 
>> model to all outcomes, using 'materials' as a moderator. One level of 
>> the 'materials' factor will become the reference level to which all 
>> other levels are compared. Using contrasts, you can then also compare 
>> all other levels against each other.
>> Best,
>> Wolfgang
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