A helpful vignette on modeling missing data in brms was posted recently: https://cran.r-project.org/web/packages/brms/vignettes/brms_missings.html
In that example, though, only one of the predictors (chl) has missing data. And Paul writes, “Since age contains no missing values, we only have to take special care of bmi and chl.”
Imagine, however, that there is missing data in both of the predictors, as in:
X1 X2 NA 10 12 13 25 22 11 NA NA NA 20 17
It is possible to imagine an imputation model that simultaneously uses X1 to impute X2 and uses X2 to impute X1. Conceptually, however, it is not clear to me what happens when both values are missing for an observation (as in the 5th row above).
What are the special considerations in this case?