Augmenting Insufficiently Accruing Oncology Clinical Trials Using Generative Models: Validation Study

Recruitment shortfalls are a major reason clinical trials fail, often leading to underpowered studies and inconclusive results. Incomplete enrollment wastes valuable resources and may expose patients to treatments with limited scientific benefit. As researchers look for solutions, real-world data and machine learning offer promising new tools.

Study Overview

This evaluation explored whether generative models—including GANs, VAEs, Bayesian networks, and sequential decision trees—could simulate missing patients in nine breast cancer clinical trials with simulated enrollment gaps. Researchers trained models on early enrollees and generated synthetic patients to complete the datasets, then tested whether the augmented trials replicated the results of the originals. Findings showed high fidelity and replicability, especially with sequential synthesis, even when up to 40% of trial participants were missing.

The study highlights a potential path to rescue underpowered trials and design smaller, more efficient studies from the start. It also reinforces the value of rigorous modeling to ensure that synthetic data supports credible, decision-grade evidence.

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