使用电子健康记录（EHR）和医疗/药品索赔等行政患者护理数据开展基于人群的科学研究已变得越来越普遍。由于巨大的样本量会导致非常小的标准误，研究人员需要更加关注所关注关联参数估计中的潜在偏倚，特别是那些不会随样本量增加而减小的偏倚。在这些多种偏倚来源中，本文重点关注对选择偏倚的理解。我们提出了一个使用有向无环图的分析框架，用于指导应用研究者剖析不同来源的选择偏倚如何影响二分类结局与所关注暴露（连续或分类）之间关联的估计。我们考虑了四种易于实施的加权方法来减少选择偏倚，并给出了相应的方差公式。我们通过模拟研究展示了它们在实践中结合真实世界数据分析何时能够发挥作用。我们使用一个数据实例比较这些方法，其中目标是估计众所周知的癌症与生物学性别之间的关联，所用数据来自密歇根大学医疗系统纵向生物样本库的 EHR。我们提供了带注释的 R 代码，以实施这些加权方法及相关推断。
英文摘要：
Using administrative patient-care data such as Electronic Health Records (EHR) and medical/ pharmaceutical claims for population-based scientific research has become increasingly common. With vast sample sizes leading to very small standard errors, researchers need to pay more attention to potential biases in the estimates of association parameters of interest, specifically to biases that do not diminish with increasing sample size. Of these multiple sources of biases, in this paper, we focus on understanding selection bias. We present an analytic framework using directed acyclic graphs for guiding applied researchers to dissect how different sources of selection bias may affect estimates of the association between a binary outcome and an exposure (continuous or categorical) of interest. We consider four easy-to-implement weighting approaches to reduce selection bias with accompanying variance formulae. We demonstrate through a simulation study when they can rescue us in practice with analysis of real world data. We compare these methods using a data example where our goal is to estimate the well-known association of cancer and biological sex, using EHR from a longitudinal biorepository at the University of Michigan Healthcare system. We provide annotated R codes to implement these weighted methods with associated inference.
A note on 'Collider bias undermines our understanding of COVID-19 disease risk and severity' and how causal Bayesian networks both expose and resolve the problem
Towards a Transportable Causal Network Model Based on Observational Healthcare Data
University of Milano - Bicocca（米兰比可卡大学）；Fondazione IRCCS Istituto Nazionale dei Tumori（意大利国家肿瘤研究所（IRCCS基金会））；F. Hoffmann - La Roche Ltd（罗氏公司）；University of Twente（特文特大学）；Istituto Dalle Molle di Studi sull’Intelligenza Artificiale (IDSIA)（达勒·莫勒人工智能研究所（IDSIA））
Overcoming Selection Bias in Statistical Studies With Amortized Bayesian Inference
University of Bonn（波恩大学）；Institut Pasteur（巴斯德研究所）；University of Freiburg（弗赖堡大学）；LMU University Hospital（慕尼黑大学附属医院）；German Center for Infection Research（德国感染研究中心）；Fraunhofer Institute ITMP（弗劳恩霍夫ITMP研究所）；Max von Pettenkofer Institute（马克斯·冯·佩滕科弗研究所）；LMU Munich（慕尼黑大学）；German Research Center for Environmental Health（德国环境健康研究中心）；Institut Necker-Enfants Malades（内克-儿童疾病研究所）
