Transportable inference using target population summary statistics under covariate shift
Academy of Mathematics and Systems Science Chinese Academy of Sciences（中国科学院数学与系统科学研究院）；Department of Biostatistics Mailman School of Public Health Columbia University（哥伦比亚大学麦尔曼公共卫生学院生物统计学系）；Department of Epidemiology & Biostatistics University of California at San Francisco（旧金山大学生物统计与流行病学系）
将研究人群的结果推导到目标人群是现实世界中基于证据决策的核心。现有大多数方法需要从两个群体中获取个体层面的数据以应对协变量偏移。然而，隐私法规和数据共享限制通常禁止访问目标人群的数据，只剩下协变量汇总信息可供分析。本文开发了可移植性方法，使能够使用源个体层面的数据和目标协变量汇总信息进行有效的推断。首先，我们应用熵平衡来实现可移植性，使源个体层面的数据能够调整以匹配目标协变量矩。我们建立了熵平衡估计量的渐近正态性，并提出了方差估计器以考虑协变量汇总的不确定性。其次，我们开发了一种新的可移植性方法，允许灵活建模协变量偏移，从而同时考虑协变量偏移和协变量汇总的不确定性。建立了所提出估计量的渐近正态性，并对其渐近方差进行一致估计。所提出的方法在应对协变量偏移方面具有更大的灵活性，因此在比熵平衡所要求的更弱的条件下允许一致估计和有效推断。所提出的方法通过模拟评估并用 Surveillance, Epidemiology, and End Results 乳腺癌数据进行说明。
英文摘要：
Transporting findings from a study population to a target population is central to evidence-based decision-making in real-world settings. Most existing methods require individual-level data from both populations to account for covariate shift. However, privacy regulations and data-sharing constraints often preclude access to such data from the target population, leaving only covariate summaries available for analysis. In this paper, we develop transportability methods that enable valid inference using source individual-level data and target covariate summaries. First, we apply entropy balancing to transportability, enabling source individual-level data to be adjusted to match the target covariate moments. We establish asymptotic normality for the entropy balancing estimator and propose a variance estimator to account for uncertainty in covariate summaries. Second, we develop a new transportability method that allows more flexible modeling of covariate shift, thereby accounting for covariate shift and uncertainty in covariate summaries simultaneously. Asymptotic normality for the proposed estimator is established and its asymptotic variance is consistently estimated. The proposed method offers greater flexibility in accounting for covariate shift and thus permits consistent estimation and valid inference when the validity of entropy balancing is not guaranteed. The proposed methods are evaluated by simulations and illustrated with an analysis of Surveillance, Epidemiology, and End Results breast cancer data.
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Heterogeneous Image-based Classification Using Distributional Data Analysis
MD Anderson Cancer Center（MD安德森癌症中心）；UT Southwestern Medical Center（西南医学中心）；UC Davis Comprehensive Cancer Center（加州大学戴维斯分校综合癌症中心）；Baylor College of Medicine（贝勒医学院）
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MRC Biostatistics Unit, University of Cambridge（剑桥大学MRC生物统计单元）；Department of Medical Statistics, London School of Hygiene & Tropical Medicine（伦敦卫生与热带医学院医学统计学系）；Royal Brompton Hospital, part of Guy’s and St Thomas’ NHS Foundation Trust（皇家布龙普顿医院（盖伊和圣托马斯NHS基金会信托旗下））；National Heart and Lung Institute, Imperial College（帝国理工学院国家心肺研究所）；Divisions of Biostatistics and Epidemiology and Pulmonary Medicine, Cincinnati Children’s Hospital Medical Center（辛辛那提儿童医院医疗中心生物统计、流行病学和肺病科）；Department of Pediatrics, University of Cincinnati（辛辛那提大学儿科系）；Department of Public Health, Policy and Systems, University of Liverpool（利物浦大学公共卫生、政策和系统系）；Cardiovascular Epidemiology Unit, University of Cambridge（剑桥大学心血管流行病学单元）；Ohio University Heritage College of Osteopathic Medicine（俄亥俄大学遗产骨科医学院）；University of Edinburgh（爱丁堡大学）
