Can synthetic data reproduce real-world findings in epidemiology? A replication study using adversarial random forests

TL;DR

This study introduces adversarial random forests (ARF) for synthesizing epidemiological data and demonstrates its effectiveness in reliably reproducing key research findings while maintaining privacy.

Contribution

The paper presents ARF as an efficient, high-quality method for generating synthetic epidemiological data, validated through replication of multiple studies and comparison with existing synthesizers.

Findings

ARF-generated data consistently replicated original study results.

Performance improved with lower dataset dimensionality and variable complexity.

ARF outperformed other synthesizers in utility, privacy, and efficiency.

Abstract

Synthetic data holds substantial potential to address practical challenges in epidemiology due to restricted data access and privacy concerns. However, many current methods suffer from limited quality, high computational demands, and complexity for non-experts. Furthermore, common evaluation strategies for synthetic data often fail to directly reflect statistical utility and measure privacy risks sufficiently. Against this background, a critical underexplored question is whether synthetic data can reliably reproduce key findings from epidemiological research while preserving privacy. We propose adversarial random forests (ARF) as an efficient and convenient method for synthesizing tabular epidemiological data. To evaluate its performance, we replicated statistical analyses from six epidemiological publications covering blood pressure, anthropometry, myocardial infarction, accelerometry, loneliness, and diabetes, from the German National Cohort (NAKO Gesundheitsstudie), the Bremen STEMI Registry U45 Study, and the Guelph Family Health Study. We further assessed how dataset dimensionality and variable complexity affect the quality of synthetic data, and contextualized ARF's performance by comparison with commonly used tabular data synthesizers in terms of utility, privacy, generalisation, and runtime. Across all replicated studies, results on ARF-generated synthetic data consistently aligned with original findings. Even for datasets with relatively low sample size-to-dimensionality ratios, replication outcomes closely matched the original results across descriptive and inferential analyses. Reduced dimensionality and variable complexity further enhanced synthesis quality. ARF demonstrated favourable performance regarding utility, privacy preservation, and generalisation relative to other synthesizers and superior computational efficiency.