Single-arm trials are an important study design for evaluating drug efficacy and safety without enrolling patients into a control arm. Although they do not provide the gold-standard evidence of randomized controlled trials, they are increasingly used in clinical development as they offer an efficient, ethical, and practical alternative. A wide variety of approaches can be used to construct control comparators and estimate treatment effects, from fixed comparators informed by clinical knowledge to data-based and model-based patient-level comparators, also known as synthetic controls. Powerful and flexible machine learning models can allow outcome-model-based synthetic controls to overcome key limitations of direct data-based approaches, yield more robust estimates of treatment effects, and provide a principled way to incorporate corrections or encode additional assumptions when external data are not directly comparable. In this work, we argue that outcome-model-based synthetic control arms are an important tool for single-arm trials. We focus on digital twins, personalized predictions of disease progression generated from machine learning models trained on historical datasets, which naturally leverage these flexible approaches. We review doubly robust estimators, present power and sample size formulas, and discuss trade-offs in selecting historical data for training and analysis. We also outline practical considerations for deploying digital twins within the framework of recent FDA draft guidance on the use of artificial intelligence in drug development. Finally, we reanalyze data from trials in amyotrophic lateral sclerosis and Huntington's disease to demonstrate the proposed methods.

翻译：单臂试验是一种重要的研究设计，用于评估药物疗效和安全性，无需将患者纳入对照组。尽管它们无法提供随机对照试验的金标准证据，但因其高效、伦理且实用的替代方案，在临床开发中的应用日益增多。构建对照比较组和估计治疗效果可采用多种方法，从基于临床知识的固定比较组，到基于数据和模型的患者级比较组（即合成对照）。强大而灵活的机器学习模型可使基于结局模型的合成对照克服直接基于数据方法的局限性，提供更稳健的治疗效果估计，并在外部数据不可直接比较时，提供一种原则性方法来纳入校正或编码额外假设。本研究认为，基于结局模型的合成对照组是单臂试验的重要工具。我们聚焦于数字孪生——基于历史数据集训练的机器学习模型生成的疾病进展个性化预测，该方法天然利用了这些灵活方法。我们综述了双稳健估计量，给出了统计功效和样本量公式，并讨论了选择历史数据用于训练和分析的权衡。我们还概述了在美国食品药品监督管理局关于药物开发中人工智能使用的最新指南草案框架内部署数字孪生的实践考量。最后，我们重新分析了肌萎缩侧索硬化症和亨廷顿病试验的数据，以展示所提出的方法。