1 UT Health, Houston, TX, USA.
2 UW School of Medicine and Public Health, Madison, WI, USA.
Stat Methods Med Res. 2019 Feb;28(2):432-444. doi: 10.1177/0962280217727033. Epub 2017 Aug 22.
This article describes a nonparametric conditional imputation analytic method for randomly censored covariates in linear regression. While some existing methods make assumptions about the distribution of covariates or underestimate standard error due to lack of imputation error, the proposed approach is distribution-free and utilizes resampling to correct for variance underestimation. The performance of the novel method is assessed using simulations, and results are contrasted with methods currently used for a limit of detection censored design, including the complete case approach and other nonparametric approaches. Theoretical justifications for the proposed method are provided, and its application is demonstrated through a study of association between lipoprotein cholesterol in offspring and parental history of cardiovascular disease.
本文描述了一种用于线性回归中随机删失协变量的非参数条件推断分析方法。虽然一些现有的方法对协变量的分布做出了假设，或者由于缺乏插补误差而低估了标准误差，但所提出的方法是无分布的，并利用重采样来纠正方差低估。通过模拟评估了新方法的性能，并将结果与目前用于检测极限删失设计的方法进行了对比，包括完全案例方法和其他非参数方法。为所提出的方法提供了理论依据，并通过研究后代脂蛋白胆固醇与父母心血管疾病史之间的关联来展示其应用。
