标题： 评估虚拟控制增强试验以复制原始RCTs中的治疗效果 显示英文标题

标题： Evaluating virtual-control-augmented trials for reproducing treatment effect from original RCTs

摘要： 这项研究探讨了使用虚拟患者数据来增强随机对照试验（RCT）中的对照组。 使用IST和IST3试验的数据，我们模拟了随机对照试验，其中对照组的招募在最初计划样本量的一部分后停止，并由通过CTGAN和TVAE生成的虚拟患者完成，这两种AI算法是在招募的对照组患者上训练的。 在IST中，14天内死亡或依赖的绝对风险差异（ARD）为-0.012（SE 0.014）。 在招募了10%和50%的参与者后，通过CTGAN生成的虚拟患者完成对照组，得到了ARD为0.004（SE 0.014）（相对差异133%）和-0.021（SE 0.014）（相对差异76%）。 结果与IST3或TVAE相当。 这是首次实证展示仅从试验数据生成虚拟对照组所伴随的错误和误导性结论的风险。 显示英文摘要

摘要： 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.