Yang Y., Land K.C. Age-Period-Cohort Analysis: New Models, Methods, and Empirical Applications

Boca Raton: CRC Press, 2013. — 322 p. — (Interdisciplinary Statistics Series). — ISBN: 978-1-4665-0753-1.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 develop along the age, period, and cohort dimensions.
The book makes two essential contributions to quantitative studies of time-related change. Through the introduction of the GLMM framework, it shows how innovative estimation methods and new model specifications can be used to tackle the "model identification problem" that has hampered the development and empirical application of APC analysis. The book also addresses the major criticism against APC analysis by explaining the use of new models within the GLMM framework to uncover mechanisms underlying age patterns and temporal trends.
Encompassing both methodological expositions and empirical studies, this book explores the ways in which statistical models, methods, and research designs can be used to open new possibilities for APC analysis. It compares new and existing models and methods and provides useful guidelines on how to conduct APC analysis. For empirical illustrations, the text incorporates examples from a variety of disciplines, such as sociology, demography, and epidemiology. Along with details on empirical analyses, software and programs to estimate the models are available on the book’s web page.Why Cohort Analysis?The Conceptualization of Cohort Effects
Distinguishing Age, Period, and Cohort SummaryAPC Analysis of Data from Three Common Research DesignsRepeated Cross-Sectional Data Designs
Research Design I:
Age:by-Time Period Tabular Array of Rates/Proportions
Research Design II: Repeated Cross-Sectional Sample Surveys
Research Design III: Prospective Cohort Panels and the Accelerated Longitudinal DesignFormalities of the Age-Period-Cohort Analysis Conundrum and a Generalized Linear Mixed Models (GLMM) FrameworkDescriptive APC Analysis
Algebra of the APC Model
Identification Problem
Conventional Approaches to the APC Identification Problem
Generalized Linear Mixed Models (GLMM) FrameworkAPC Accounting/Multiple Classification Model, Part I: Model Identification and Estimation Using the Intrinsic EstimatorAlgebraic, Geometric, and Verbal Definitions of the Intrinsic Estimator
Statistical Properties
Model Validation: Empirical Example
Model Validation: Monte Carlo Simulation Analyses
Interpretation and Use of the Intrinsic EstimatorAPC Accounting/Multiple Classification Model, Part II: Empirical ApplicationsRecent U.S. Cancer Incidence and Mortality Trends by Sex and Race: A Three-Step Procedure
APC Model-Based Demographic Projection and ForecastingMixed Effects Models: Hierarchical APC-Cross-Classified Random Effects Models (HAPC-CCREM), Part I: The BasicsBeyond the Identification Problem
Basic Model Specification
Fixed versus Random Effects HAPC Specifications
Interpretation of Model Estimates
Assessing the Significance of Random Period and Cohort Effects
Random Coefficients HAPC-CCREMMixed Effects Models: Hierarchical APC-Cross-Classified Random Effects Models (HAPC-CCREM), Part II: Advanced AnalysesLevel 2 Covariates: Age and Temporal Changes in Social Inequalities in Happiness
HAPC-CCREM Analysis of Aggregate Rate Data on Cancer Incidence and Mortality
Full Bayesian Estimation
HAPC-Variance Function RegressionMixed Effects Models: Hierarchical APC-Growth Curve Analysis of Prospective Cohort DataIntercohort Variations in Age Trajectories
Intracohort Heterogeneity in Age Trajectories
Intercohort Variations in Intracohort Heterogeneity PatternsDirections for Future Research and ConclusionAdditional Models
Longitudinal Cohort Analysis of Balanced Cohort Designs of Age Trajectories