In the analysis of event history data, some covariates may vary over time. Such covariates are typically sparse and measured intermittently in many longitudinal settings. This article considers additive hazard regression models with intermittently measured covariates and proposes kernel weighting methods to estimate the model parameters. Two scenarios are investigated: half kernel estimation for data in which observation ceases at an event, and full kernel estimation in which observation may continue after an event, such as in recurrent event data. The asymptotic properties of the resulting estimators are established. Simulation studies show that the proposed estimators perform satisfactorily. An application to a study on primary biliary cirrhosis is illustrated.

中文翻译：

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具有间歇测量协变量的事件历史研究的加性风险回归

在分析事件历史数据时，一些协变量可能会随时间而变化。这样的协变量通常是稀疏的，并且在许多纵向设置中间歇性地测量。本文考虑了具有间歇测量协变量的加性风险回归模型，并提出了核加权方法来估计模型参数。研究了两种情况：在事件中观察停止的数据的半核估计，以及在事件之后可能继续观察的全核估计，例如在重复事件数据中。结果估计量的渐近性质被建立。仿真研究表明，所提出的估计器表现令人满意。说明了在原发性胆汁性肝硬化研究中的应用。