We study the multiplicative hazards model with intermittently observed longitudinal covariates and time-varying coefficients. For such models, the existing {\it ad hoc} approach, such as the last value carried forward, is biased. We propose a kernel weighting approach to get an unbiased estimation of the non-parametric coefficient function and establish asymptotic normality for any fixed time point. Furthermore, we construct the simultaneous confidence band to examine the overall magnitude of the variation. Simulation studies support our theoretical predictions and show favorable performance of the proposed method. A data set from cerebral infarction is used to illustrate our methodology.

翻译：我们研究了一种具有间歇性观测纵向协变量和时变系数的乘法风险模型。针对这类模型，现有的启发式方法（如末次观测值结转法）存在偏倚。我们提出了一种核加权方法，以获取非参数系数函数的无偏估计，并建立了任意固定时间点的渐近正态性。此外，我们构建了同时置信带以检验变异的整体幅度。模拟研究支持了我们的理论预测，并显示出所提方法的优异性能。我们使用来自脑梗塞的数据集来阐明该方法的应用。