  Much of the micro data used for epidemiological studies contain sensitive
measurements on real individuals. As a result, such micro data cannot be
published out of privacy concerns, rendering any published statistical analyses
on them nearly impossible to reproduce. To promote the dissemination of key
datasets for analysis without jeopardizing the privacy of individuals, we
introduce a cohesive Bayesian framework for the generation of fully synthetic,
high dimensional micro datasets of mixed categorical, binary, count, and
continuous variables. This process centers around a joint Bayesian model that
is simultaneously compatible with all of these data types, enabling the
creation of mixed synthetic datasets through posterior predictive sampling.
Furthermore, a focal point of epidemiological data analysis is the study of
conditional relationships between various exposures and key outcome variables
through regression analysis. We design a modified data synthesis strategy to
target and preserve these conditional relationships, including both
nonlinearities and interactions. The proposed techniques are deployed to create
a synthetic version of a confidential dataset containing dozens of health,
cognitive, and social measurements on nearly 20,000 North Carolina children.
