The Cox proportional hazards model is a statistical technique used to analyze the time until an event occurs, such as death or failure. It helps researchers understand the relationship between the survival time of subjects and one or more predictor variables, often referred to as covariates. This model assumes that the effect of the covariates on the hazard rate is constant over time. One key feature of the Cox model is that it does not require the assumption of a specific baseline hazard function, making it flexible for various types of survival data. It is widely used in fields like medicine, epidemiology, and social sciences to evaluate risk factors and treatment effects.