In epidemiological research, the relationships between risk factors and health outcomes are often complex and non-linear. For example, the effect of air pollution on respiratory diseases may not be linear across different levels of exposure. Proc GAM can capture these intricate patterns more effectively than traditional models, leading to better understanding and more accurate predictions.
