[R-meta] Correction for sample overlap in a meta-analysis of prevalence
th@obr@wn @end|ng |rom gm@||@com
Tue Aug 4 15:25:31 CEST 2020
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 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
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 The
authors proposed FOLD, a method to optimize power in a meta-analysis of
genetic associations studies with overlapping subjects.
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.
*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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