School of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China.
Department of Biostatistics, Columbia University, New York, New York, USA.
Biometrics. 2023 Sep;79(3):2036-2049. doi: 10.1111/biom.13723. Epub 2022 Aug 4.
Over the past decade, there has been growing enthusiasm for using electronic medical records (EMRs) for biomedical research. Quantile regression estimates distributional associations, providing unique insights into the intricacies and heterogeneity of the EMR data. However, the widespread nonignorable missing observations in EMR often obscure the true associations and challenge its potential for robust biomedical discoveries. We propose a novel method to estimate the covariate effects in the presence of nonignorable missing responses under quantile regression. This method imposes no parametric specifications on response distributions, which subtly uses implicit distributions induced by the corresponding quantile regression models. We show that the proposed estimator is consistent and asymptotically normal. We also provide an efficient algorithm to obtain the proposed estimate and a randomly weighted bootstrap approach for statistical inferences. Numerical studies, including an empirical analysis of real-world EMR data, are used to assess the proposed method's finite-sample performance compared to existing literature.
在过去的十年中，人们越来越热衷于将电子病历 (EMR) 用于生物医学研究。分位数回归估计分布关联，为 EMR 数据的复杂性和异质性提供了独特的见解。然而，EMR 中广泛存在的不可忽略的缺失观测值常常掩盖了真实的关联，并挑战了其在稳健的生物医学发现中的潜力。我们提出了一种在分位数回归下存在不可忽略缺失响应时估计协变量效应的新方法。该方法对响应分布没有施加任何参数规范，而是巧妙地利用了相应分位数回归模型所诱导的隐含分布。我们证明了所提出的估计量是一致的和渐近正态的。我们还提供了一种有效的算法来获得所提出的估计值，以及一种随机加权引导方法来进行统计推断。数值研究，包括对真实 EMR 数据的实证分析，用于评估与现有文献相比，所提出方法的有限样本性能。
