Background: Adaptive interventions provide a guide for using ongoing information about individuals to decide whether and how to modify the type, amount, delivery modality, or timing of treatment, to improve intervention effectiveness while reducing cost and burden. The variables that inform treatment modification decisions are called tailoring variables. Specifying a tailoring variable requires describing what should be measured, when to measure it, when the measure should be used to make decisions, and what cutoffs should be used in making decisions. These questions are causal and prescriptive (what to do, when), not merely predictive. They involve tradeoffs between specificity and sensitivity, and between waiting for sufficient information versus intervening quickly. Purpose: There is little specific guidance in the literature on how to empirically choose tailoring variables, including cutoffs, measurement times, and decision times. Methods: We review possible approaches for comparing potential tailoring variables and propose a framework for systematically developing tailoring variables. Results: Although secondary observational data can be used to select tailoring variables, additional assumptions are needed. A specifically designed experiment for optimization (an optimization randomized controlled trial), e.g., a multi-arm randomized trial, sequential multiple assignment randomized trial, factorial experiment, or hybrid design, may provide a more direct way to answer these questions. Conclusions: Using randomization directly to inform tailoring variables provides the most direct causal evidence but requires more effort and resources than secondary data analysis. More research is needed on how best to design tailoring variables for effective, scalable interventions.

翻译：背景：自适应干预为利用个体持续信息以决定是否及如何调整治疗的类型、剂量、实施方式或时机提供了指导框架，旨在提升干预效果的同时降低成本和负担。用于指导治疗调整决策的变量称为定制变量。确定定制变量需要明确测量内容、测量时机、决策使用时机以及决策阈值设定。这些问题具有因果性与规范性（何时采取何种行动），而不仅仅是预测性。其涉及特异性与敏感性之间的权衡，以及等待充分信息与快速干预之间的平衡。目的：现有文献中关于如何通过实证方法选择定制变量（包括阈值、测量时间与决策时间）的具体指导较为缺乏。方法：本文综述了比较潜在定制变量的可能方法，并提出系统化构建定制变量的框架。结果：虽然可利用二手观察数据选择定制变量，但需要额外假设条件。专门设计的优化实验（如优化随机对照试验），例如多臂随机试验、序贯多重分配随机试验、析因实验或混合设计，可能为回答这些问题提供更直接的途径。结论：直接利用随机化证据指导定制变量能提供最直接的因果证据，但相较于二手数据分析需要更多投入与资源。未来需进一步研究如何优化设计定制变量以构建有效且可扩展的干预方案。