Age-period-cohort analysis: new models, methods, and empirical applications

Content: Introduction Why Cohort Analysis? Introduction The Conceptualization of Cohort Effects Distinguishing Age, Period, and Cohort Summary APC Analysis of Data from Three Common Research Designs Introduction Repeated 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 Design Formalities of the Age-Period-Cohort Analysis Conundrum and a Generalized Linear Mixed Models (GLMM) Framework Introduction Descriptive APC Analysis Algebra of the APC Model Identification Problem Conventional Approaches to the APC Identification Problem Generalized Linear Mixed Models (GLMM) Framework APC Accounting/Multiple Classification Model, Part I: Model Identification and Estimation Using the Intrinsic Estimator Introduction Algebraic, 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 Estimator APC Accounting/Multiple Classification Model, Part II: Empirical Applications Introduction Recent U.S. Cancer Incidence and Mortality Trends by Sex and Race: A Three-Step Procedure APC Model-Based Demographic Projection and Forecasting Mixed Effects Models: Hierarchical APC-Cross-Classified Random Effects Models (HAPC-CCREM), Part I: The Basics Introduction Beyond 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-CCREM Mixed Effects Models: Hierarchical APC-Cross-Classified Random Effects Models (HAPC-CCREM), Part II: Advanced Analyses Introduction Level 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 Regression Mixed Effects Models: Hierarchical APC-Growth Curve Analysis of Prospective Cohort Data Introduction Intercohort Variations in Age Trajectories Intracohort Heterogeneity in Age Trajectories Intercohort Variations in Intracohort Heterogeneity Patterns Summary Directions for Future Research and Conclusion Introduction Additional Models Longitudinal Cohort Analysis of Balanced Cohort Designs of Age Trajectories Conclusion Index References appear at the end of each chapter.