长新冠观察性研究中辅助变量依赖抽样的逆概率加权法
Inverse probability weighting for auxiliary variable dependent sampling in observational studies of Long COVID
AI总结：
针对长新冠观察性研究中辅助变量依赖抽样的偏差问题，结合RECOVER队列研究，提出用逆概率加权法处理该抽样设计以避免估计偏差。
AI中文摘要：
基于辅助变量值的选择性检测是观察性研究中日益流行的设计策略，在电子健康记录数据中普遍存在。忽略这种潜在抽样机制会导致估计偏差和错误的科学结论。然而，用于处理观察性环境中两阶段抽样设计的严格分析方法仍未得到充分利用。受“研究新冠以促进康复（RECOVER）”成人与儿科观察性队列研究的启发，我们描述了常见的陷阱以及分析通过辅助变量依赖抽样收集的数据的方法。
英文摘要：
Selective testing based on values of auxiliary variables is an increasingly popular design strategy in observational studies and is ubiquitous in electronic health record data. Ignoring this underlying sampling mechanism can lead to biased estimation and erroneous scientific conclusions. Yet, rigorous analytic methods for accounting for two-phase sampling designs in observational settings remain under-utilized. Motivated by the Researching COVID to Enhance Recovery (RECOVER) Adult and Pediatric observational cohort studies, we describe common pitfalls and an approach for analysis of data collected via auxiliary variable dependent sampling.
