Missingness mechanism messiness in meta-analysis: A response to "The importance of plausible data-generating mechanisms in simulation studies".

The current paper responds to the commentary on the article (doi:10.1002/jrsm.1605). We discuss our perspectives on the missing data mechanisms and models assumed and used in our simulation study while acknowledging the inherent generalizability limitations of any (simulation) study. The plausibility of the exact missing data mechanism is challenging to definitively identify in any applied dataset. We describe and justify our assumed scenario in meta-regression that we investigated. We also revisit the performance of the deletion method and how it is tied into the assumed missingness model. Lastly, we reiterate the importance of conducting sensitivity analyses assessing different ways of handling missing data given different assumptions and offer this study as a starting point for future research.