[R-meta] Correction for sample overlap in a meta-analysis of prevalence
th@obr@wn @end|ng |rom gm@||@com
Thu Aug 6 14:37:35 CEST 2020
Thanks a lot for your clear response.
I totally agree that the information on the degree of overlapping is not
I will take a look at the cluster-robust inference you mentioned.
On Thu, Aug 6, 2020 at 2:23 PM Viechtbauer, Wolfgang (SP) <
wolfgang.viechtbauer using maastrichtuniversity.nl> wrote:
> Dear Thao,
> I do not know these papers, so I cannot comment on what methods they
> describe and whether those could be implemented using metafor.
> Obviously, the degree of dependence between overlapping estimates depends
> on the degree of overlap. Say there are two diseases (as in your example).
> Then if we had the raw data, we could count the number of individuals that:
> x1: have only disease 1
> x2: have only disease 2
> x12: have both disease 1 and 2
> x0: have neither disease
> Let n = x1 + x2 + x12 + x0. Then you have p1 = (x1+x12) / n and p2 =
> (x2+x12) / n as the two prevalences. One could easily work out the
> covariance (I am too lazy to do that right now), but in the end this won't
> help, because computing this will require knowing all the x's, not just p1
> and p2 and n. And I assume no information is reported on the degree of
> overlap. One could maybe make some reasonable 'guestimates' and then
> compute the covariances followed by a sensitivity analysis.
> Alternatively, you could use the 'sandwich' method (cluster-robust
> inference). This has been discussed on this mailing list extensively in the
> past (not in the context of overlap in such estimates, but the principle is
> all the same).
> >-----Original Message-----
> >From: R-sig-meta-analysis [mailto:
> r-sig-meta-analysis-bounces using r-project.org]
> >On Behalf Of Thao Tran
> >Sent: Tuesday, 04 August, 2020 15:26
> >To: r-sig-meta-analysis using r-project.org
> >Subject: [R-meta] Correction for sample overlap in a meta-analysis of
> >Dear all,
> > I want to conduct a meta-analysis of around 30 studies (from a systematic
> >Some background of the studies: The quantity of interest is the prevalence
> >of RSV infection. Different studies reported RSV prevalence for different
> >risk groups. Since, it is quite often that some people might suffer from
> >multiple comorbidities (for example, an individual might have both cardiac
> >disease and lung disease), and it was not stated clearly in the reported
> >data if these two sub-populations (cardia disease patients, and lung
> >disease patients) are mutually exclusive. In the end, I want to have an
> >overall estimate across all risk groups. Given the fact stated above, it
> >likely that some of the data (from two or more risk groups) might share a
> >proportion of the population. For example, John's study reported data on
> >cardiac disease as well as lung disease. These two risk groups were
> >included in the meta-analysis. However, we need to take into account the
> >fact that, the two sub-populations might share some proportions of
> >I was searching on the internet methods to account for the overlap samples
> >while conducting meta-analysis. There are two papers that address this
> > 1. https://academic.oup.com/bioinformatics/article/33/24/3947/3980249
> > authors proposed FOLD, a method to optimize power in a meta-analysis of
> > genetic associations studies with overlapping subjects.
> > 2.
> > this paper, the author compared generalized weights and
> > weights meta-estimates to account for overlap sample.
> >My question is:
> >Are these approaches incorporated into the *metafor* package?
> >Thanks for your input.
> >*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
*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
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