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 data (from two or more risk groups) might share a proportion of population. For example: John 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 overlap sample while conducting meta-analysis. There are two papers that address this problem:
- https://academic.oup.com/bioinformatics/article/33/24/3947/3980249 The authors proposed FOLD, a method to optimize power in meta-analysis of genetic associations studies with overlapping subjects.
- 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 availably common packages for meta-analysis. One of my favorite is the metafor package?
- Do you know also other sources/ methods to deal with this problem, especially when the outcome of interest is either the prevalence and the incidence rate.
