Targeted Learning: Toward a Future Informed by Real-World Evidence

加权 倾向得分匹配 非参数统计 因果推理 计算机科学 真实世界的证据 置信区间 匹配(统计) 透明度(行为) 点估计 风险分析(工程) 计量经济学 机器学习 精算学 医学 统计 数学 业务 计算机安全 内科学 放射科
作者
Susan Gruber,Rachael V. Phillips,Hana Lee,Martin Ho,John Concato,Mark J. van der Laan
出处
期刊:Statistics in Biopharmaceutical Research [Taylor & Francis]
卷期号:16 (1): 11-25 被引量:9
标识
DOI:10.1080/19466315.2023.2182356
摘要

The 21st Century Cures Act of 2016 includes a provision for the U.S. Food and Drug Administration10.13039/100000038 (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, including in support of regulatory decision-making. This article presents two case studies that illustrate the utility of following the roadmap. We used 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. Nonparametric sensitivity analyses illuminate how departures from (untestable) causal assumptions 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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