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

[Thao Tran]

Hi Wolfgang,
I believe that I am now properly registered to the mailing list.
Thanks for your clarification. It helps a lot.

Have a good day.
Thao

On Tue, Jun 16, 2020 at 11:38 AM Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:

> Dear Thao,
>
> Could you please properly register yourself on this mailing list? This was
> now the 4th post of yours that had to manually approved by the mailing list
> admins and this is creating extra work for us.
>
> If the authors used Poisson regression (assuming a log link, which is the
> default), then this would be identical to computing the CI based on the
> log(rate) and then exponentiating. The use of robust variance estimation
> though implies that the SE used for constructing the CI is not the one we
> would construct based on theory, so this introduces a bit of an
> inconsistency. Ignoring this for now, the SE of a log(rate) is
> sqrt(1/numer_of_cases). We know the rate per 1000py (19.6) and the
> corresponding CI (16.2 to 23.6), so if we assume 5.5 * 1000py, we then get
> roughly the same CI:
>
> round(exp(log(19.6) + c(-1,1) * qnorm(.975) * sqrt(1/(19.6*5.5))), 2)
>
> (this gives 16.23 to 23.67 -- you can play around with the 5.5 a bit more
> to see if you can find a better approximation).
>
> So, this implies 19.6 * 5.5 = 107.8 =~ 108 cases in 5500 person-years.
>
> I don't know if these numbers are realistic based on how the study was
> conducted (that's a rather high number of PYs compared to the other
> studies), but this is what the CI implies.
>
> Best,
> Wolfgang
>
> >-----Original Message-----
> >From: Thao Tran [mailto:thaobrawn using gmail.com]
> >Sent: Tuesday, 16 June, 2020 10:36
> >To: Viechtbauer, Wolfgang (SP)
> >Cc: r-sig-meta-analysis using r-project.org
> >Subject: Re: [R-meta] Include a study with point estimate and 95% CI into
> a
> >meta-anlaysis for incidence rates
> >
> >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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