2025/07/21 by Alex Fernandes, Raphaël Porcher, Fernandes, Alex +5 · 1 citation
Decision Sciences · Mathematics · #Advanced Causal Inference Techniques #Applications (stat.AP) #FOS: Computer and information sciences #J.3 #Meta-analysis and systematic reviews #Methodology (stat.ME) #Statistical Methods in Clinical Trials
paper · pdf · doi:10.48550/arxiv.2507.16048
openalex publication_date 2025/07/21 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This study investigates the use of virtual patient data to augment control arms in randomised controlled trials (RCTs). Using data from the IST and IST3 trials, we simulated RCTs in which the recruitment in the control arms would stop after a fraction of the initially planned sample size, and would be completed by virtual patients generated by CTGAN and TVAE, two AI algorithms trained on the recruited control patients. In IST, the absolute risk difference(ARD) on death or dependency at 14 days was -0.012 (SE 0.014). Completing the control arm by CTGAN-generated virtual patients after the recruitment of 10% and 50% of participants, yielded an ARD of 0.004 (SE 0.014) (relative difference 133%) and -0.021 (SE 0.014) (relative difference 76%), respectively. Results were comparable with IST3 or TVAE. This is the first empirical demonstration of the risk of errors and misleading conclusions associated with generating virtual controls solely from trial data.