Comparer des méthodes

	Age-Period-Cohort Analysis	Life-Course Epidemiology
Domaine	Social Epidemiology	Social Epidemiology
Famille	Regression model	Process / pipeline
Année d'origine	1983	2002
Auteur d'origine	Theodore R. Holford; Yang Yang & Kenneth C. Land (intrinsic estimator)	Yoav Ben-Shlomo & Diana Kuh
Type	Generalized linear model for rates indexed by age, period, and cohort	Conceptual and analytic framework for long-term exposure-disease modeling
Source fondatrice	Holford, T. R. (1983). The Estimation of Age, Period and Cohort Effects for Vital Rates. Biometrics, 39(2), 311-324.	Ben-Shlomo, Y., & Kuh, D. (2002). A life course approach to chronic disease epidemiology: conceptual models, empirical challenges and interdisciplinary perspectives. International Journal of Epidemiology, 31(2), 285-293.
Alias	APC Analysis, Age-Period-Cohort Models, Cohort Analysis of Rates, Intrinsic Estimator APC	Life Course Approach to Chronic Disease, Life-Course Framework, Developmental Origins Epidemiology, Biological and Social Programming Approach
Apparentées	4	3
Résumé	Age-period-cohort (APC) analysis decomposes variation in disease or mortality rates into three temporal components: the effect of age (biological and accumulated risk), the effect of period (influences hitting everyone alive at a given calendar time, such as a new treatment or a recession), and the effect of cohort (lasting imprints of the conditions into which a birth generation was born). Theodore Holford's 1983 Biometrics paper gave the canonical generalized-linear-model formulation and exposed the method's defining obstacle: because cohort equals period minus age, the three predictors are exactly linearly dependent, so their individual linear slopes cannot be separately identified. A large methodological literature has since proposed constraints, reparameterizations, and estimators to extract whatever the data can legitimately support. Yang, Schulhofer-Wohl, Fu, and Land's 2008 work popularized the intrinsic estimator, a principled choice among the infinitely many fitting solutions. APC analysis is a workhorse of descriptive epidemiology and demography, used to read the temporal fingerprints left on rates of cancer, suicide, obesity, and mortality. Done carefully it separates signal from artifact; done carelessly it manufactures trends that the identification problem makes unknowable.	Life-course epidemiology is the study of how physical and social exposures across gestation, childhood, adolescence, and adult life shape later health and disease risk. Codified by Yoav Ben-Shlomo and Diana Kuh in their 2002 International Journal of Epidemiology paper and the 2003 glossary by Kuh, Ben-Shlomo, Lynch, Hallqvist, and Power, the framework supplies a set of competing conceptual models that specify how the timing and sequence of exposures matter. Rather than asking only what causes disease, it asks when exposures act and how their effects compound. Its three signature models — critical or sensitive periods, accumulation of risk, and chains of risk — give researchers a disciplined way to translate developmental and social theory into testable longitudinal hypotheses about the origins of adult chronic disease.