  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.
