Data analysis in longitudinal studies often involves complex statistical methods to account for repeated measures and potential confounding variables. Techniques such as mixed-effects models and time-to-event analysis are commonly used. These methods help in understanding the progression of exposure and its effects on health outcomes, allowing for the identification of critical exposure windows and susceptible populations.
