有针对性的学习：通过现实世界的证据走向未来
Targeted learning: Towards a future informed by real-world evidence
摘要：
2016年的《21世纪治愈法案》包括一项规定，要求美国食品药品监督管理局（FDA）评估真实世界证据（RWE）的潜在用途，以支持先前批准的药物的新适应症，并满足批准后研究要求。从真实世界数据（RWD）中提取可靠证据通常因缺乏治疗随机化、潜在并发事件和失访信息而变得复杂。有针对性的学习（TL）是统计学的一个子领域，它提供了一个严格的框架来帮助应对这些挑战。TL路线图提供了一个逐步的指南来生成有效的证据并评估其可靠性。遵循这些步骤可以产生大量信息，用于评估研究是否提供了支持监管决策的可靠科学证据。本文介绍了两个案例研究，说明了遵循路线图的效用。我们使用基于目标最小损失的估计结合超级学习来估计因果效应。我们还比较了这些结果与未经调整的分析，倾向得分匹配，和逆概率加权。非参数敏感性分析说明偏离（不可检验）因果假设将如何影响点估计值和置信区间界限，从而影响从研究中得出的实质性结论。TL从数据中学习的全面方法提供了透明度，使RWE在必要时获得信任。
英文摘要：
The 21st Century Cures Act of 2016 includes a provision for the U.S. Food and Drug Administration (FDA) to evaluate the potential use of real-world evidence (RWE) to support new indications for use for previously approved drugs, and to satisfy post-approval study requirements. Extracting reliable evidence from real-world data (RWD) is often complicated by a lack of treatment randomization, potential intercurrent events, and informative loss to follow up. Targeted Learning (TL) is a sub-field of statistics that provides a rigorous framework to help address these challenges. The TL Roadmap offers a step-by-step guide to generating valid evidence and assessing its reliability. Following these steps produces an extensive amount of information for assessing whether the study provides reliable scientific evidence in support regulatory decision making. This paper presents two case studies that illustrate the utility of following the roadmap. We use targeted minimum loss-based estimation combined with super learning to estimate causal effects. We also compared these findings with those obtained from an unadjusted analysis, propensity score matching, and inverse probability weighting. Non-parametric sensitivity analyses illuminate how departures from (untestable) causal assumptions would affect point estimates and confidence interval bounds that would impact the substantive conclusion drawn from the study. TL's thorough approach to learning from data provides transparency, allowing trust in RWE to be earned whenever it is warranted.
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