[R-meta] Include a study with point estimate and 95% CI into a meta-anlaysis for incidence rates

[Thao Tran]

Hi Wolfgang,
I looked back to the paper, there they used Poisson regression with
analytic weights, offsets, and robust variance estimation to implement the
extrapolation and standardization procedures for estimating seasonal
incidence and 95% confidence intervals (CIs).
I will need to lookup more. But my guess is it is not straightforward to
trace back these two pieces of information.

Best,

On Mon, Jun 15, 2020 at 4:44 PM Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:

> Dear Thao,
>
> You could try to back-calculate the number of cases and total person time
> from the reported results. Do you have any information how the CI (16.2 to
> 23.6) was computed? It is not symmetric around the point estimate (19.6),
> so it might have been computed based on the log incidence rate or a Poisson
> regression model using a log link. But there are other ways of computing
> such a CI, for example using the square-root transformed rate or using the
> Freeman-Tukey transformation. So, any indication how the authors actually
> computed the CI would be useful.
>
> Best,
> Wolfgang
>
> >-----Original Message-----
> >From: R-sig-meta-analysis [mailto:
> r-sig-meta-analysis-bounces using r-project.org]
> >On Behalf Of Thao Tran
> >Sent: Monday, 15 June, 2020 16:25
> >To: r-sig-meta-analysis using r-project.org
> >Subject: [R-meta] Include a study with point estimate and 95% CI into a
> >meta-anlaysis for incidence rates
> >
> >ATTACHMENT(S) REMOVED: dat2C.RData
> >
> >Dear,
> >I want to perform a meta-analysis for some studies with the interest lies
> in
> >incidence rates.
> >Many of them, the data on the number of positive cases and person-time are
> >available.
> >However, I have one study where the authors only reported point estimate
> >with its 95%CI.
> >How do I include this study into the meta-analysis using the metafor
> >package?
> >
> >Here is an example code.
> >
> >load("dat2C.RData")
> >datx <- subset(dat2C, point == 1)
> >estimS <- escalc(measure="IRLN", xi=Num, ti=py2/1000,
> >                 data=datx, slab=paste(Cite))
> >summary(estimS, transf=exp)[8:13]
> >resS <- rma( yi, vi, data=estimS, method="ML")
> >hetS <- cbind(round( resS$QE,1),round( resS$QEp,2), round( resS$I2))
> >hetS # 96%
> >
> >## However, how to include this study where point estimate (Inc)
> >## and 95% CI (Incll = lower bound, Incul = upper bound) were reported
> >xx <- subset(dat2C, point==0); dim(xx)
> >
> >I look forward to hearing from you.
> >Regards,
> >Thao
> >
> >--
> >Trần Mai Phương Thảo
> >Master Student - Master of Statistics
> >Hasselt University - Belgium.
> >Email: Thaobrawn using gmail.com / maiphuongthao.tran using student.uhasselt.be
> >Phone number: + 84 979 397 410+ 84 979 397 410 / 0032 488 0358430032 488
> >035843
>


-- 
*Trần Mai Phương Thảo*
Master Student - Master of Statistics
Hasselt University - Belgium.
Email: Thaobrawn using gmail.com / maiphuongthao.tran using student.uhasselt.be
Phone number: + 84 979 397 410+ 84 979 397 410 / 0032 488 0358430032 488
035843

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