An Online Meta-Level Adaptive Design Framework with Targeted Learning Inference: Applications to Evaluating and Utilizing Surrogate Outcomes in Adaptive Designs
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结合目标学习推断的在线元层级自适应设计：代理结局的评价与利用
- 研究问题
- 论文研究在自适应实验中，如何实时评价并选择候选分配设计，尤其衡量不同代理结局对学习速度与参与者结果的作用。
- 方法与思路
- 作者定义评价自适应设计的因果目标量，构造适应数据依赖的目标最大似然估计，再用于在线选择由不同代理结局指导的设计。
- 材料陈述的贡献
- 摘要称，该框架统一量化候选设计的收益与成本，并通过渐近正态推断评价代理结局在效应异质性检测和分配更新中的用途。
- 阅读关注点
- 应核对未实施候选设计的目标量如何识别、依赖数据下的渐近条件，以及在线设计选择与后续推断之间是否存在反馈影响。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未核实；摘要未列出识别及渐近条件，不使用参数化假设也不意味着完全无需假设。
原文摘要
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.