Synthetic data generation for a longitudinal cohort study -- Evaluation,
  method extension and reproduction of published data analysis results
Lisa Kühnel Julian Schneider Ines Perrar Tim Adams Fabian Prasser Ute Nöthlings Holger Fröhlich Juliane Fluck
Access to individual-level health data is essential for gaining new insights
and advancing science. In particular, modern methods based on artificial
intelligence rely on the availability of and access to large datasets. In the
health sector, access to individual-level data is often challenging due to
privacy concerns. A promising alternative is the generation of fully synthetic
data, i.e. data generated through a randomised process that have similar
statistical properties as the original data, but do not have a one-to-one
correspondence with the original individual-level records. In this study, we
use a state-of-the-art synthetic data generation method and perform in-depth
quality analyses of the generated data for a specific use case in the field of
nutrition. We demonstrate the need for careful analyses of synthetic data that
go beyond descriptive statistics and provide valuable insights into how to
realise the full potential of synthetic datasets. By extending the methods, but
also by thoroughly analysing the effects of sampling from a trained model, we
are able to largely reproduce significant real-world analysis results in the
chosen use case.