[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 at 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 at r-project.org]
On Behalf Of Thao Tran Sent: Monday, 15 June, 2020 16:25 To: r-sig-meta-analysis at 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
--
Tr?n Mai Ph??ng Th?o
Master Student - Master of Statistics
Hasselt University - Belgium.
Email: Thaobrawn at gmail.com / maiphuongthao.tran at student.uhasselt.be
Phone number: + 84 979 397 410+ 84 979 397 410 / 0032 488 0358430032 488
035843
*Tr?n Mai Ph??ng Th?o* Master Student - Master of Statistics Hasselt University - Belgium. Email: Thaobrawn at gmail.com / maiphuongthao.tran at student.uhasselt.be Phone number: + 84 979 397 410+ 84 979 397 410 / 0032 488 0358430032 488 035843 [[alternative HTML version deleted]]