Background: Adaptive interventions provide a guide for how ongoing information about individuals should be used to decide whether and how to modify 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 for an intervention 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, raising important tradeoffs between specificity versus 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 on how best to design tailoring variables for effective, scalable interventions is needed.

翻译：背景：自适应干预为如何利用个体持续信息来决定是否及如何调整治疗类型、剂量、传递方式或时机提供了指导，旨在提高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。为干预指定定制变量需要描述应测量什么、何时测量、何时使用测量结果进行决策，以及在决策中应采用何种阈值。这些问题具有因果性和规范性（何时应做什么），而不仅仅是预测性，需要在特异性与敏感性之间，以及等待充分信息与快速干预之间进行重要权衡。目的：现有文献中关于如何实证选择定制变量（包括阈值、测量时间和决策时间）的具体指导较少。方法：我们回顾了比较潜在定制变量的可能方法，并提出了一个系统开发定制变量的框架。结果：虽然可利用二手观察数据选择定制变量，但需要额外的假设。专门为优化设计的实验（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：直接利用随机化来指导定制变量能提供最直接的因果证据，但比二手数据分析需要更多的努力和资源。未来需要更多关于如何为有效、可扩展的干预措施设计最佳定制变量的研究。