Longitudinal data is often encountered by researchers, allowing them to use the time dimension to uncover deeper insights than what is possible with a traditional cross-sectional snapshot. But this advantage comes at a cost: the assumption that each observation is independent is broken due to the fact that patients are measured on multiple occasions over time. Failure to account for this feature when analyzing data can result in bias, and longitudinal methods should be used to account for this problem. Two powerful but simple solutions are the fixed and random effects estimators, both of which have recently become more popular in medical research. A key feature of both is that they model the unobserved differences between patients, and can even control for unobserved confounding. This course will discuss the methods and intuition behind both modelling techniques, alongside practical examples and interactive sessions in STATA. Attendees will gain both knowledge and practical skills in this course. Although not essential, those who have STATA loaded on their laptops are encouraged to bring your laptop in order to participate in interactive sessions.