Meta-analyzing incidence (count) data with many zero cells I am conducting a meta-analysis on the incidence of a rare event. I have 15 single-arm studies with events occurring in only 2 studies.
The metarate function in the meta package allows for meta-analysis of incidence, but the only option I can find for managing zero-count data is a fixed increase in zero data. I would rather use a more-evidence based approach such an alternative continuity correction.
Is there a way to do alternative continuity correction with metarate? I am open to other evidence-based methods and other package solutions.

PS I considered logistic regression as there are many strategies for zero-count data (zero inflated, negative binomial, and hurdle models) but I am dissuaded by the fact that I have no important covariates because this analysis is at the level of the study, not the patient.

The principal reason for using weights in meta-analysis is because it is feared that studies have different precisions and low precision studies should not have too much weight. However if the event rate is similar between studies and the study size is comparable between studies then that argument becomes weaker and a simple marginal approach (ie aggregating over studies) may well be defensible.
In an ideal world you would do a simulation along the lines of "Much ado about nothing: a comparison of the performance of meta--analytical methods with rare events" https://doi.org/10.1002/sim.2528 to check. They do briefly mention marginal methods in the discussion but in the context of meta-analysis of comparative studies.
