Longitudinal data, where the subjects are repeatedly observed over time, have a unique capability of investigating the changes in disease risks and time-varying associations between risk exposures and health outcomes. By incorporating parametric modelling structures and flexible nonparametric coefficient curves, the time-varying coefficient models are a class of natural tools that describe the dynamic relationships of longitudinal variables. However, conventional time-varying coefficient models, which assume that the covariates affect the outcome instantaneously, are unable to evaluate the time-lagged covariate effects. We propose a class of time-lagging varying-coefficient models, which extend the conventional models to incorporate the covariate exposure over an unknown lagged time, and develop a spline-based procedure to estimate the time-lagging coefficient curves and select the appropriate lagging time. We demonstrate the performance of our proposed model through a simulation study and an application to the longitudinal biomarker data from a clinical trial of hematopoietic stem cell transplantation.