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

Dr. Gerta Rücker ruecker @end|ng |rom |mb|@un|-|re|burg@de
Thu Aug 6 17:32:45 CEST 2020

Dear Thao,

Another Paper by Pedro Bom and Heiko Rachinger ("A Generalized-Weights 
Solution to Sample Overlap in Meta-Analysis") will soon appear in 
Research Synthesis Methods (early view). You may have a look at it when 
it will be published.



Am 06.08.2020 um 14:37 schrieb Thao Tran:
> Hi Wolfgang,
> Thanks a lot for your clear response.
> I totally agree that the information on the degree of overlapping is not
> commonly reported.
> I will take a look at the cluster-robust inference you mentioned.
> Best,
> Thao
> 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).
>> 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

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