Epidemiological data often involve non-linear relationships and interactions that are difficult to capture with simple linear models. For example, the effect of air pollution on health outcomes may vary non-linearly with levels of pollution, temperature, and other environmental factors. GAMs can model these relationships more accurately by using smoothing functions to capture the non-linearity. This leads to better understanding and more accurate predictions of health outcomes.
