Many real-world health datasets violate one of statistics’ biggest assumptions: independence. In this episode, we explain why repeated and correlated data break standard statistical methods—and how biostatistics handles it. You’ll learn what longitudinal data are, how longitudinal study designs differ from cross-sectional studies, and why models like mixed-effects models, generalized estimating equations (GEE), and hierarchical models are essential for analyzing repeated measurements in public health and medical research.

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