In biomedical studies, we are often interested in the association between different types of covariates and the times to disease events. Because the relationship between the covariates and event times is often complex, standard survival models that assume a linear covariate effect are inadequate. A flexible class of models for capturing complex interaction effects among types of covariates is the varying coefficient models, where the effects of a type of covariates can be modified by another type of covariates. In this paper, we study kernel-based estimation methods for varying coefficient additive hazards models. Unlike many existing kernel-based methods that use a local neighborhood of subjects for the estimation of the varying coefficient function, we propose a novel global approach that is generally more efficient. We establish theoretical properties of the proposed estimators and demonstrate their superior performance compared with existing local methods through large-scale simulation studies. To illustrate the proposed method, we provide an application to a motivating cancer genomic study.

翻译：在生物医学研究中，我们常关注不同类型协变量与疾病事件发生时间之间的关联。由于协变量与事件时间的关系往往较为复杂，标准生存模型中假设线性协变量效应的设定存在局限性。变系数模型作为一种灵活模型类型，能够捕获协变量间复杂的交互效应——其中某类协变量的效应可受到另一类协变量影响。本文研究变系数加性风险模型的核估计方法。不同于许多现有基于局部邻域受试者数据估计变系数函数的核方法，我们提出了一种全局估计新方法，该方法通常具有更高效率。我们建立了所提估计量的理论性质，并通过大规模模拟研究证明其相较于现有局部方法的优越性能。为说明该方法的应用，我们将其应用于一项具有启发性的癌症基因组学研究。