Randomized controlled trials (RCTs) often suffer from limited sample sizes due to high costs and lengthy recruitment periods, compromising precision in treatment effect estimation. External real-world control data offer a valuable opportunity for augmentation, but naïve integration may introduce bias without careful compatibility assessment. This paper presents a practical tutorial on the adaptive influence-based borrowing framework~\citep{Yang-etal2026}, which addresses this challenge through a principled, individual-level borrowing strategy. The core intuition is straightforward: rather than indiscriminately pooling all external controls (ECs), the framework first asks how much each external patient would perturb the outcome model fitted using RCT controls. External patients whose inclusion barely changes this model are deemed comparable and prioritized for borrowing, whereas those who substantially shift it are flagged as potentially incompatible. This individual-level compatibility metric, based on the influence score, is then used to construct a sequence of nested candidate subsets of ECs, from which the optimal subset is selected by minimizing the mean squared error of the treatment effect estimator, balancing the competing risks of bias from over-borrowing and imprecision from under-borrowing. When systematic differences between ECs and RCT controls are substantial, an optional outcome calibration step can align the two groups before influence-based selection proceeds. We provide a clear, step-by-step workflow with emphasis on methodological intuition, practical considerations, and visualization, thereby offering a principled, transparent, and practical method for leveraging ECs when RCTs alone are underpowered. Implementation is supported by an accompanying \texttt{R} package InfluenceBorrowing.

翻译：随机对照试验（RCT）常因高成本和漫长招募期导致样本量有限，从而削弱了治疗效应估计的精确性。外部真实世界对照数据为增强分析提供了宝贵机会，但若无谨慎的相容性评估，简单整合可能引入偏倚。本文介绍了一个关于自适应影响借用框架的实用教程（引用自Yang等人2026），该框架通过一种有原则的个体层面借用策略应对这一挑战。其核心直觉简明直接：并非不加区分地合并所有外部对照（EC），而是首先评估每个外部患者会对仅使用RCT对照拟合的结果模型产生多大扰动。那些加入后几乎不改变模型的EC被视为可比较的，并优先借用；而那些显著改变模型的EC则被标记为可能不相容。基于影响分数的个体层面相容性指标，用于构建一系列嵌套的EC候选子集，进而通过最小化治疗效应估计量的均方误差来选择最优子集，从而在过度借用导致的偏倚风险与借用不足导致的不精确性之间取得平衡。当EC与RCT对照间存在显著系统性差异时，可在基于影响的筛选前进行可选的结局校准步骤以对齐两组数据。我们提供了清晰的逐步工作流程，重点强调方法论直觉、实践考量和可视化，从而为RCT单独不足以提供足够统计效能时利用EC提供了一种有原则、透明且实用的方法。实现由配套的R包InfluenceBorrowing支持。