In many diseases, the occurrence of a clinical event (e.g., stroke) can change the risk of other, usually more serious, events (e.g., death). Quantifying the impact of the first event and factoring this into assessment or extrapolation of the risk of the second event is important for clinical understanding of the disease as well as health economic assessments of new treatments. For instance, in oncology, understanding the impact of progression of disease on the risk of dying can be vital for projection of overall survival, which is often only partially observed in trials. Proper understanding of the impact of the event requires consideration of not only the occurrence of the event, but also its timing and the possibility that its effect changes with time following the event. For instance, patients who progress early after initiation of treatment may be subject to a greater increase in risk of death than a patient who progresses, say, a year after treatment. Similarly, once progressed, the increase in mortality may be highest soon after the event and gradually decline among surviving patients. We will outline a statistical modeling framework designed to quantify these various dimensions of the impact of events using Cox regression models with time-dependent covariates and effects (i.e., coefficients) to reflect the timing of event (TE) as well as time elapsed since the event (TSE). The model produces estimates that have direct clinical relevance; for instance, the coefficient for TE provides an assessment of the benefit of delaying progression, while TSE reflects whether and for how long the increase in mortality is sustained and whether it ever returns to the level of patients who had not progressed. The parameterization of the model will be illustrated with example code and analyses of example data.