Interval-censored covariates are frequently encountered in biomedical studies, particularly in time-to-event data or when measurements are subject to detection or quantification limits. Yet, the estimation of regression models with interval-censored covariates remains methodologically underdeveloped. In this article, we address the estimation of generalized linear models when one covariate is subject to interval censoring. We propose a likelihood-based approach, GELc, that builds upon an augmented version of Turnbull's nonparametric estimator for interval-censored data. We prove that the GELc estimator is consistent and asymptotically normal under mild regularity conditions, with available standard errors. Simulation studies demonstrate favorable finite-sample performance of the estimator and satisfactory coverage of the confidence intervals. Finally, we illustrate the method using two real-world applications: the AIDS Clinical Trials Group Study 359 and an observational nutrition study on circulating carotenoids. The proposed methodology is available as an R package at github.com/atoloba/ICenCov.

翻译：区间删失协变量在生物医学研究中经常出现，尤其常见于时间-事件数据或测量值受检测限/定量限约束的情形。然而，针对含区间删失协变量的回归模型的估计方法在方法论层面仍发展不足。本文致力于研究当某一协变量存在区间删失时广义线性模型的估计问题。我们提出一种基于似然的估计方法GELc，该方法建立在Turnbull区间删失数据非参数估计器的增强版本之上。我们证明在温和的正则性条件下，GELc估计器具有一致性和渐近正态性，且可获得标准误。模拟研究展示了该估计器在有限样本下的优良性能及置信区间的满意覆盖水平。最后，我们通过两个实际应用案例说明该方法：艾滋病临床试验组359研究及关于循环类胡萝卜素的观察性营养学研究。所提出的方法已封装为R软件包，发布于github.com/atoloba/ICenCov。