Bayesian Data Synthesis and the Utility-Risk Trade-Off for Mixed Epidemiological Data

TL;DR

This paper introduces a Bayesian framework for generating fully synthetic mixed-type epidemiological micro datasets that preserve key relationships while protecting individual privacy, facilitating reproducible research.

Contribution

It develops a joint Bayesian model compatible with various data types and a synthesis strategy that maintains conditional relationships, advancing privacy-preserving data sharing.

Findings

Successfully created a synthetic dataset of 20,000 children with preserved relationships.

Demonstrated the method's ability to maintain complex nonlinear and interaction effects.

Enabled reproducible epidemiological analysis without compromising privacy.

Abstract

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.