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Dear Todor,<br>
<br>
I cannot comment (in detail) on using <b>metafor</b>, however,
using transf.ipft.hm() is surely wrong, as this is for
back-transformation of proportions, not incidence rates. You have to
use transf.iirpf() for the inverse of the Freeman-Tukey
transformation of incidence rates. Note, you have to use the
function with two "i"s where the first "i" stands for "inverse" and
the second "i" stands for "incidence". Using transf.irpf() with one
"i" would be wrong.<br>
<br>
<br>
Alternatively, you could use function metarate() from <b>meta</b>
which does the back-transformation automatically for you.
Furthermore, metarate() has an argument 'irscale' to display results
are events per x person-years. e.g., 'irscale = 100' to report
events per 100 person-years.<br>
<br>
m.irft <- metarate(xi, ti, data = dat, studlab = study, sm =
"IRFT", method.tau = "PM", irscale = 100, prediction = TRUE)<br>
summary(m.irft)<br>
forest(m.irft, xlim = c(0, 5))<br>
<br>
As you see in the attached forest plot, there is a problem with the
Freeman-Tukey (back-)transformation:<br>
the overall estimates (fixed effect and random effects) are very
small which does not make much sense. This results from problems
with the back-transformation in this setting with many studies with
zero events.<br>
<br>
Therefore, I would recommend to use a different method for the
meta-analysis. Personally, I would use metarate() with argument
'method = "GLMM"' which conducts a random intercept Poisson
regression model (Stijnen et al. 2010, section 3.3). Note,
metarate() calls rma.glmm() from <b>metafor</b> internally to
conduct the Poisson regression.<br>
<br>
The pooled results for the Poisson regression<br>
<br>
0.73 events (fixed effect) and 0.70 events (random effects) per 100
person-years<br>
<br>
look much more plausible to me than the results for the
Freeman-Tukey method<br>
<br>
0.15 events (fixed effect) and 0.16 events (random effects) per 100
person-years.<br>
<br>
Best wishes,<br>
Guido<br>
<br>
<br>
Reference:<br>
<br>
Stijnen, T., Hamza, T.H. & Ozdemir, P., 2010, Random effects
meta-analysis of event outcome in the framework of the generalized
linear mixed model with applications in sparse data, Statistics in
Medicine, 29(29), pp. 3046-67<br>
<pre class="moz-signature" cols="72">--
Dr. Guido Schwarzer
Institute of Medical Biometry and Statistics,
Faculty of Medicine and Medical Center - University of Freiburg
Postal address: Stefan-Meier-Str. 26, D-79104 Freiburg
Phone: +49/761/203-6668
Mail: <a class="moz-txt-link-abbreviated" href="mailto:sc@imbi.uni-freiburg.de">sc@imbi.uni-freiburg.de</a>
Homepage: <a class="moz-txt-link-freetext" href="http://www.imbi.uni-freiburg.de">http://www.imbi.uni-freiburg.de</a>
</pre>
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