Adaptive designs are increasingly used in clinical trials and online experiments to improve participant outcomes by dynamically updating treatment allocation as data accumulate. In practice, experimenters often consider multiple candidate designs, each with distinct trade-offs. However, typically only one design is implemented at a time, leaving benefits and costs of alternative designs unobserved and unquantified. To address this, we propose a novel meta-level adaptive design framework that enables real-time, data-driven evaluation and selection among candidate adaptive designs. Specifically, we define a new class of causal estimands to evaluate adaptive designs and propose Targeted Maximum Likelihood Estimators for these estimands. These estimators are asymptotically normal while accommodating dependence in adaptive-design data without parametric assumptions, enabling online selection among candidate designs. We further apply this framework to a motivating example where multiple surrogates of a long-term outcome are considered for updating randomization probabilities in adaptive experiments. Unlike existing surrogate evaluation methods, our approach comprehensively quantifies surrogates' utility to accelerate detection of heterogeneous treatment effects, expedite updates to treatment randomization, and improve participant outcomes, facilitating dynamic selection among surrogate-guided designs. Overall, our framework provides a unified approach for evaluating opportunities and costs of various adaptive designs and guiding real-time decision-making in adaptive experiments.

翻译：自适应设计在临床试验和在线实验中应用日益广泛，通过随数据积累动态更新治疗分配来改善参与者结局。实践中，实验者常考虑多种候选设计，每种设计具有不同的权衡特征。然而，通常每次仅实施一种设计，导致替代设计的收益与成本无法观测且难以量化。为此，我们提出一种新颖的元级自适应设计框架，能够实时、数据驱动地评估和选择候选自适应设计。具体而言，我们定义了一类新的因果估计量来评估自适应设计，并提出针对这些估计量的目标最大似然估计量。这些估计量在无需参数假设的条件下能够适应自适应设计数据中的相关性，且具有渐近正态性，从而支持候选设计间的在线选择。我们进一步将该框架应用于一个激励性案例：该案例考虑利用长期结局的多个替代指标来更新自适应实验中的随机化概率。与现有替代指标评估方法不同，我们的方法全面量化了替代指标在加速检测异质性处理效应、加快处理随机化更新以及改善参与者结局等方面的效用，从而促进替代指标引导设计的动态选择。总体而言，本框架为评估各类自适应设计的机遇与成本、指导自适应实验中的实时决策提供了统一方法。