  Derived variables are variables that are constructed from one or more source
variables through established mathematical operations or algorithms. For
example, body mass index (BMI) is a derived variable constructed from two
source variables: weight and height. When using a derived variable as the
outcome in a statistical model, complications arise when some of the source
variables have missing values. In this paper, we propose how one can define a
single fully Bayesian model to simultaneously impute missing values and sample
from the posterior. We compare our proposed method with alternative approaches
that rely on multiple imputation, and, with a simulated dataset, consider how
best to estimate the risk of microcephaly in newborns exposed to the ZIKA
virus.
