Includes bibliographical references and index.

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1. Introduction -- 2. Why cohort analysis? -- 3. APC analysis of data from three common research designs -- 4. Formalities of the age-period-cohort analysis conundrum and a generalized linear mixed models (GLMM) framework -- 5. APC accounting/multiple classification model, part I : model identification and estimation using the intrinsic estimator -- 6. APC accounting/multiple classification model, part II : empirical applications -- 7. Mixed effects models : hierarchical APC-cross-classified random effects models (HAPC-CCREM), part I : the basics -- 8. Mixed effects models : hierarchical APC-cross-classified random effects models (HAPC-CCREM), part II : advanced analyses -- 9. Mixed effects models : hierarchical APC-growth curve analysis of prospective cohort data -- 10. Directions for future research and conclusion.

Age-Period-Cohort Analysis: New Models, Methods, and Empirical Applications is based on a decade of the authors' collaborative work in age-period-cohort (APC) analysis. Within a single, consistent HAPC-GLMM statistical modeling framework, the authors synthesize APC models and methods for three research designs: age-by-time period tables of population rates or proportions, repeated cross-section sample surveys, and accelerated longitudinal panel studies. The authors show how the empirical application of the models to various problems leads to many fascinating findings on how outcome variables d.

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