Synthetic data as external control arms in scarce single-arm clinical trials

An external control arm based on health registry data can serve as an alternative comparator in single-arm drug development studies that lack a benchmark for comparison to the experimental treatment. However, accessing such observational healthcare data involves a lengthy and intricate application process, delaying drug approval studies and access to novel treatments. Clinical trials typically comprise only a few hundred patients usually with high-cardinality features, which makes individual data instances more exposed to re-identification attacks. We examine whether synthetic data can serve as a proxy for the empirical control arm data by providing the same research outcomes while reducing the risk of information disclosure. We propose a reversible data generalization procedure to address these particular data characteristics that can be used in conjunction with any generator algorithm. It reduces the input data cardinality pre-synthesis and reverses it post-synthesis to regain the original data structure. Finally, we test a selection of state-of-the-art generators against a suite of utility and privacy metrics. The external control arm benchmark was generated using data from Norwegian health registries. In this retrospective study, we compare various synthetic data generation algorithms in numerical experiments, focusing on the utility of the synthetic data to support the conclusions drawn from the empirical data, and analysing the risk of sensitive information disclosure. Our results indicate that data generalization is advantageous to enhance both data utility and privacy in smaller datasets with high cardinality. Moreover, the generator algorithms demonstrate the ability to generate synthetic data of high utility without compromising the confidentiality of the empirical data. Our finding suggests that synthetic external control arms could serve as a viable alternative to observational data in drug development studies, while reducing the risk of revealing sensitive patient information.Author summary: In our research, we explored how synthetically generated data can serve as a comparison benchmark to experimental treatments in drug development studies. Such studies often face challenges in accessing a benchmark for clinical trials, delaying the evaluation and potentially the access to novel treatments. We examined whether synthetic data can serve as a proxy for an empirical benchmark, providing the same study evaluation results while reducing the risk of disclosing patient information. In our approach, we introduced a method to simplify the data before creating synthetic versions and then reconstruct the original structure after synthesis. We compared various synthetic data generation techniques to see if they could effectively replicate the utility of real data without compromising patient privacy. Our findings show that synthetic data can indeed match the usefulness of empirical data while reducing the risk of revealing sensitive patient information. This suggests that synthetic data could be a practical alternative in drug development. Such an advancement could speed up the process of evaluating and bringing new treatments to market while enhancing patient privacy.