How can we understand patterns of prescription use?

Background Longitudinal analysis is important as due to the temporal sequence of exposure then outcome, we can make a stronger case for causality. A derivative of a class of models that fit into the ‘data-mining’ family is sequence analysis. One use of this model is to understand lifetime states, e.g. being employed, being in education, being retired. By understanding these state sequences we can understand how the duration and timing of a state can affect health in the long term.