Toward Valid Generative Clinical Trial Data with Survival Endpoints
This work addresses the problem of slow and costly clinical trials for oncology and rare diseases by proposing a generative AI approach to create synthetic control arms, though it is incremental as it builds on existing methods with improvements in calibration.
The paper tackles the challenge of generating synthetic clinical trial data with survival endpoints, which are difficult to model due to censoring and small sample sizes, by introducing a VAE-based method that outperforms GAN baselines on fidelity, utility, and privacy metrics, while revealing and addressing calibration issues.
Clinical trials face mounting challenges: fragmented patient populations, slow enrollment, and unsustainable costs, particularly for late phase trials in oncology and rare diseases. While external control arms built from real-world data have been explored, a promising alternative is the generation of synthetic control arms using generative AI. A central challenge is the generation of time-to-event outcomes, which constitute primary endpoints in oncology and rare disease trials, but are difficult to model under censoring and small sample sizes. Existing generative approaches, largely GAN-based, are data-hungry, unstable, and rely on strong assumptions such as independent censoring. We introduce a variational autoencoder (VAE) that jointly generates mixed-type covariates and survival outcomes within a unified latent variable framework, without assuming independent censoring. Across synthetic and real trial datasets, we evaluate our model in two realistic scenarios: (i) data sharing under privacy constraints, where synthetic controls substitute for original data, and (ii) control-arm augmentation, where synthetic patients mitigate imbalances between treated and control groups. Our method outperforms GAN baselines on fidelity, utility, and privacy metrics, while revealing systematic miscalibration of type I error and power. We propose a post-generation selection procedure that improves calibration, highlighting both progress and open challenges for generative survival modeling.