Kenneth C. Land

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.

This book presents an innovative demographic toolkit known as the ProFamy extended cohort-component method for the projection of household structures and living arrangements with empirical applications to the United States, the largest developed country, and China, the largest developing country. The ProFamy method uses demographic rates as inputs to project detailed distributions of household types and sizes, living arrangements of all household members, and population by age, sex, race/ethnicity, and urban/rural residence at national, sub-national, or small area levels. It can also project elderly care needs and costs, pension deficits, and household consumption. The ProFamy method presented herein has substantial merits compared to the traditional headship rate method, which is not linked to demographic rates and projects limited household types without other household members than "heads".The book consists of four parts. The first part presents the methodology, data, estimation issues, and empirical assessments. The next parts present applications in the United States (part two) and China (part three), concerning demographic, social, economic, and business research; policy analysis, including forecasting future trends of household type/size, elderly living arrangements, disability, and home-based care costs, and household consumption including housing and vehicles. The fourth part includes a user’s guide for the ProFamy software to project households, living arrangements, and home-based consumptions.This book offers an invaluable toolkit for researchers, analysts and students in academic, public and private businesses, whose work is related to levels and rates of change in households, population and consumption patterns.

This book presents an innovative demographic toolkit known as the ProFamy extended cohort-component method for the projection of household structures and living arrangements with empirical applications to the United States, the largest developed country, and China, the largest developing country. The ProFamy method uses demographic rates as inputs to project detailed distributions of household types and sizes, living arrangements of all household members, and population by age, sex, race/ethnicity, and urban/rural residence at national, sub-national, or small area levels. It can also project elderly care needs and costs, pension deficits, and household consumption. The ProFamy method presented herein has substantial merits compared to the traditional headship rate method, which is not linked to demographic rates and projects limited household types without other household members than "heads".The book consists of four parts. The first part presents the methodology, data, estimation issues, and empirical assessments. The next parts present applications in the United States (part two) and China (part three), concerning demographic, social, economic, and business research; policy analysis, including forecasting future trends of household type/size, elderly living arrangements, disability, and home-based care costs, and household consumption including housing and vehicles. The fourth part includes a user’s guide for the ProFamy software to project households, living arrangements, and home-based consumptions.This book offers an invaluable toolkit for researchers, analysts and students in academic, public and private businesses, whose work is related to levels and rates of change in households, population and consumption patterns.

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.

Supported by real-world case studies, this essential textbook provides a detailed overview of the use of biostatistical tools and methods, enabling students and researchers to undertake their own research with confidence and understanding.After a general introduction to the field, the book provides a step-by-step description of the essential statistical methods that are foundational to analyzing data from clinical trials, epidemiological studies, and other health-related research. From basic concepts such as probability and distribution through to hypothesis testing, regression analysis, survival analysis, meta analysis and systematic reviews, each chapter is designed with a clear pedagogical approach featuring explanatory diagrams, real-life examples and sample problems. Later sections of the book cover clinical trial design and analysis, diagnostic testing, Bayesian methods and machine learning. Through this detailed, comprehensive treatment of the key tools and methods, the book encourages readers to develop their own critical thinking skills, recognising good or bad pieces of research when they see them, asking questions about where evidence and assumptions come from, or choosing the most appropriate biostatistical methodologies in their own research.Written by a team of experts with extensive teaching experience in this field, this is the ideal textbook for graduate students and researchers across the biomedical sciences, from Public Health to Epidemiology to Clinical Medicine.

Supported by real-world case studies, this essential textbook provides a detailed overview of the use of biostatistical tools and methods, enabling students and researchers to undertake their own research with confidence and understanding.After a general introduction to the field, the book provides a step-by-step description of the essential statistical methods that are foundational to analyzing data from clinical trials, epidemiological studies, and other health-related research. From basic concepts such as probability and distribution through to hypothesis testing, regression analysis, survival analysis, meta analysis and systematic reviews, each chapter is designed with a clear pedagogical approach featuring explanatory diagrams, real-life examples and sample problems. Later sections of the book cover clinical trial design and analysis, diagnostic testing, Bayesian methods and machine learning. Through this detailed, comprehensive treatment of the key tools and methods, the book encourages readers to develop their own critical thinking skills, recognising good or bad pieces of research when they see them, asking questions about where evidence and assumptions come from, or choosing the most appropriate biostatistical methodologies in their own research.Written by a team of experts with extensive teaching experience in this field, this is the ideal textbook for graduate students and researchers across the biomedical sciences, from Public Health to Epidemiology to Clinical Medicine.

This volume is a critical exposition of the data and analyses from a full decade of rigorous research into how age-related changes at the individual level, along with other factors, contribute to morbidity, disability and mortality risks at the broader population level. After summarizing the state of our knowledge in the field, individual chapters offer enlightening discussion on a range of key topics such as age trajectory analysis in select and general populations, incidence/age patterns of major chronic illnesses, and indices of cumulative deficits and their use in characterizing and understanding the detailed properties of individual aging.The book features comprehensive statistical analyses of unique longitudinal data sets including the unique resource of the Framingham Heart Study, with its more than 60 years of follow-up. Culminating in penetrating conclusions about the insights gained from the work involved, this book adds much to our understanding of the links between aging and human health.

This volume is a critical exposition of the data and analyses from a full decade of rigorous research into how age-related changes at the individual level, along with other factors, contribute to morbidity, disability and mortality risks at the broader population level. After summarizing the state of our knowledge in the field, individual chapters offer enlightening discussion on a range of key topics such as age trajectory analysis in select and general populations, incidence/age patterns of major chronic illnesses, and indices of cumulative deficits and their use in characterizing and understanding the detailed properties of individual aging.The book features comprehensive statistical analyses of unique longitudinal data sets including the unique resource of the Framingham Heart Study, with its more than 60 years of follow-up. Culminating in penetrating conclusions about the insights gained from the work involved, this book adds much to our understanding of the links between aging and human health.