[R-meta] Correction for sample overlap in a meta-analysis of prevalence

[Viechtbauer, Wolfgang (SP)]

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).

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: 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
>prevalence
>
>Dear all,
>
> I want to conduct a meta-analysis of around 30 studies (from a systematic
>review).
>
>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 is
>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
>participants.
>
>I was searching on the internet methods to account for the overlap samples
>while conducting meta-analysis. There are two papers that address this
>problem:
>
>   1. https://academic.oup.com/bioinformatics/article/33/24/3947/3980249 The
>   authors proposed FOLD, a method to optimize power in a meta-analysis of
>   genetic associations studies with overlapping subjects.
>   2.
>   http://www.stiftung.at/wp-content/uploads/2015/04/BomPaper_Oct_2014.pdf
>In
>   this paper, the author compared generalized weights and inverse-variance
>   weights meta-estimates to account for overlap sample.
>
>My question is:
>
>Are these approaches incorporated into the *metafor* package?
>Thanks for your input.
>Best,
>
>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