标题： 使用元学习器评估现实世界数据中的替代异质性 显示英文标题

标题： Assessing Surrogate Heterogeneity in Real World Data Using Meta-Learners

摘要： 替代标志物通常是在随机临床试验的背景下研究的。然而，对替代结局的需求不仅限于这些情境，在现实世界中的公共卫生和社会科学领域研究中，这种需求可能更为突出，因为随机试验往往不切实际。针对真实世界非随机数据中识别替代标志物的研究很少，因为现有的评估替代标志物的统计方法倾向于依赖治疗随机化的假设。尽管少数允许非随机治疗/暴露的方法能够适当地处理混杂的个体特征，但它们无法考察替代标志物与患者特征之间的异质性。本文提出了一种框架，用于评估真实世界（即非随机）数据中替代标志物的异质性，并使用多种元学习器实现这一框架。我们的方法能够在考虑混杂因素的同时，量化替代标志物强度与患者特征之间的异质性，同时通过灵活的现成机器学习方法来适应混杂因素。此外，我们利用该框架来识别那些替代标志物可以作为主要结局有效替代的个体。我们通过模拟研究以及应用到评估糖化血红蛋白作为空腹血浆葡萄糖替代标志物的异质性来检验我们方法的表现。 显示英文摘要

摘要： Surrogate markers are most commonly studied within the context of randomized clinical trials. However, the need for alternative outcomes extends beyond these settings and may be more pronounced in real-world public health and social science research, where randomized trials are often impractical. Research on identifying surrogates in real-world non-randomized data is scarce, as available statistical approaches for evaluating surrogate markers tend to rely on the assumption that treatment is randomized. While the few methods that allow for non-randomized treatment/exposure appropriately handle confounding individual characteristics, they do not offer a way to examine surrogate heterogeneity with respect to patient characteristics. In this paper, we propose a framework to assess surrogate heterogeneity in real-world, i.e., non-randomized, data and implement this framework using various meta-learners. Our approach allows us to quantify heterogeneity in surrogate strength with respect to patient characteristics while accommodating confounders through the use of flexible, off-the-shelf machine learning methods. In addition, we use our framework to identify individuals for whom the surrogate is a valid replacement of the primary outcome. We examine the performance of our methods via a simulation study and application to examine heterogeneity in the surrogacy of hemoglobin A1c as a surrogate for fasting plasma glucose.