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Life sciences · Journal article
Advances in Data Analysis and Classification · November 2, 2026
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Abstract To provide a practical guide to the Age-Period-Cohort (APC) model’s core concepts, assumptions, and applications in biostatistics, epidemiology, and demography, helping researchers interpret complex temporal dynamics in health outcomes such as cancer incidence, mortality, and infectious disease trends. To address the major challenge of identifiability in APC analysis where age, period, and cohort effects are linearly dependent—advanced statistical approaches are employed. These include constrained generalized linear models (CGLIMs), the Intrinsic Estimator (IE), Hierarchical APC (HAPC) models, and restricted cubic splines within generalized linear mixed models (GLMMs). Each approach allows disentangling of the three effects to reveal underlying drivers of disease patterns. The APC model has proven effective in public health research. It distinguishes age-related risks from period-based shifts (e.g., due to medical advances) and cohort-specific behaviors (e.g., smoking initiation). Applications range from analyzing long-term cancer incidence trends to understanding infectious disease dynamics and evaluating policy impacts such as tobacco control or vaccination campaigns. The APC model supports evidence-based public health strategies by isolating the independent contributions of aging, historical events, and generational changes. Its continued use enables more targeted interventions and robust evaluations of health policies over time.