Age-period-cohort analysis: new models, methods, and empirical applications
Gespeichert in:
| Beteiligte Personen: | , |
|---|---|
| Format: | Buch |
| Sprache: | Englisch |
| Veröffentlicht: |
Boca Raton
CRC Press, Taylor & Francis Group
[2013]
|
| Schriftenreihe: | Interdisciplinary statistics
|
| Schlagwörter: | |
| Links: | https://doi.org/10.1201/b13902 http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026927293&sequence=000001&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026927293&sequence=000002&line_number=0002&func_code=DB_RECORDS&service_type=MEDIA |
| Umfang: | xiii, 338 Seiten Diagramme |
| ISBN: | 9781466507524 |
| DOI: | 10.1201/b13902 |
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| 245 | 1 | 0 | |a Age-period-cohort analysis |b new models, methods, and empirical applications |c Yang Yang and Kenneth C. Land |
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Datensatz im Suchindex
| _version_ | 1819290289457594368 |
|---|---|
| adam_text | Contents
1 Introduction.....................................................1
References.......................................................5
2 Why Cohort Analysis?.............................................7
2.1 Introduction................................................7
2.2 The Conceptualization of Cohort Effects.....................7
2.3 Distinguishing Age, Period, and Cohort......................9
2.4 Summary....................................................12
References......................................................13
3 APC Analysis of Data from Three Common Research Designs.........15
3.1 Introduction...............................................15
3.2 Repeated Cross-Sectional Data Designs......................15
3.3 Research Design I: Age-by֊Time Period Tabular Array of
Rates/Proportions..........................................19
3.3.1 Understanding Cancer Incidence and Mortality
Using APC Analysis: Biodemography, Social
Disparities, and Forecasting........................19
3.3.2 Cancer Incidence Rates from Surveillance,
Epidemiology, and End Results (SEER): 1973-2008.....21
3.3.3 Cancer Mortality Rates from the National Center for
Health Statistics (NCHS): 1969-2007.................21
3.4 Research Design II: Repeated Cross-Sectional Sample Surveys... 26
3.4.1 General Social Survey (GSS) 1972-2006: Verbal Test
Score and Subjective Well-Being.....................26
3.4.2 National Health and Nutrition Examination Surveys
(NHANES) 1971-2008: The Obesity Epidemic............32
3.4.3 National Health Interview Surveys (NHIS) 1984-2007:
Health Disparities.........................................34
3.4.4 Birth Cohort and Time Period Covariates Related to
Cancer Trends.......................................37
3.5 Research Design III: Prospective Cohort Panels and the
Accelerated Longitudinal Design............................39
3.5.1 Americans Changing Lives (ACL) Study 1986-2002:
Depression, Physical Disability, and Self-Rated Health... 41
3.5.2 Health and Retirement Survey (HRS) 1992-2008:
Frailty Index.......................................48
References......................................................50
4 Formalities of the Age-Period-Cohort Analysis Conundrum and
a Generalized Linear Mixed Models (GLMM) Framework...............55
4.1 Introduction................................................55
4.2 Descriptive APC Analysis....................................56
4.3 Algebra of the APC Model Identification Problem.............61
4.4 Conventional Approaches to the APC Identification Problem..63
4.4.1 Reduced Two-Factor Models............................64
4.4.2 Constrained Generalized Linear Models (CGLIMs).......65
4.4.3 Nonlinear Parametric Transformation..................66
4.4.4 Proxy Variables......................................66
4.4.5 Other Approaches in Biostatistics....................67
4.5 Generalized Linear Mixed Models (GLMM) Framework............68
References.......................................................71
5 APC Accounting/Multiple Classification Model, Part I: Model
Identification and Estimation Using the Intrinsic Estimator......75
5.1 Introduction................................................75
5.2 Algebraic, Geometric, and Verbal Definitions of the Intrinsic
Estimator...................................................76
5.2.1 Algebraic Definition.................................77
5.2.2 Geometric Representation.............................80
5.2.3 Verbal Description...................................82
5.2.4 Computational Tools..................................83
5.3 Statistical Properties.................................... 84
5.3.1 Estimability, Unbiasedness, and Relative Efficiency..84
5.3.2 Asymptotic Properties................................86
5.3.3 Implications.........................................87
5.4 Model Validation: Empirical Example.........................89
5.5 Model Validation: Monte Carlo Simulation Analyses...........92
5.5.1 Results for APC Models: True Effects of A, P, and C
All Present........................................ 94
5.5.1.1 Property of Estimable Constraints............98
5.5.2 Misuse of APC Models: Revisiting a Numerical
Example.............................................104
5.6 Interpretation and Use of the Intrinsic Estimator..........109
Appendix 5.1: Proof of Unbiasedness of the IE as an Estimator of
the b0 = Pprojb Constrained APC Coefficient Vector.........115
Appendix 5.2: Proof of Relative Efficiency of the IE as an Estimator
of the b0 = Pprojb Constrained APC Coefficient Vector......116
Appendix 5.3: IE as a Minimum Norm Quadratic Unbiased
Estimator of the b0 = Pprojb Constrained APC Coefficient Vector... 117
Appendix 5.4: Interpreting the Intrinsic Estimator, Its Relationship
to Other Constrained Estimators in APC Accounting
Models, and Limits on Its Empirical Applicability..........118
References.......................................................120
6 APC Accounting/Multiple Classification Model, Part II:
Empirical Applications...........................................125
6.1 Introduction................................................125
6.2 Recent U.S. Cancer Incidence and Mortality Trends by Sex
and Race: A Three-Step Procedure...........................125
6.2.1 Step 1: Descriptive Analysis Using Graphics........126
6.2.2 Step 2: Model Fit Comparisons......................146
6.2.3 Step 3: IE Analysis.................................152
6.2.3.1 All Cancer Sites Combined...................153
6.2.3.2 Age Effects by Site.........................156
6.2.3.3 Period Effects by Site......................161
6.2.3.4 Cohort Effects on Cancer Incidence..........165
6.2.3.5 Cohort Effects on Cancer Mortality..........166
6.2.4 Summary and Discussion of Findings..................167
6.3 APC Model-Based Demographic Projection and Forecasting.... 169
6.3.1 Two-Dimensional versus Three-Dimensional View ...... 170
6.3.2 Forecasting of the U.S. Cancer Mortality Trends for
Leading Causes of Death.............................171
6.3.2.1 Methods of Extrapolation....................171
6.3.2.2 Prediction Intervals........................172
6.3.2.3 Internal Validation.........................173
6.3.2.4 Forecasting Results.........................181
Appendix 6.1: The Bootstrap Method Using a Residual
Resampling Scheme for Prediction Intervals.................188
References.......................................................189
7 Mixed Effects Models: Hierarchical APC-Cross-Classified
Random Effects Models (HAPC-CCREM), Part I: The Basics...........191
7.1 Introduction................................................191
7.2 Beyond the Identification Problem...........................192
7.3 Basic Model Specification...................................195
7.4 Fixed versus Random Effects HAPC Specifications.............199
7.5 Interpretation of Model Estimates...........................205
7.6 Assessing the Significance of Random Period and Cohort
Effects....................................................208
7.6.1 HAPC Linear Mixed Models............................209
7.6.1.1 Step 1: Study the Patterns and Statistical
Significance of the Individual Estimated
Coefficients for Time Periods and Birth
Cohorts.....................................209
7.6.1.2 Step 2: Test for the Statistical Significance
of the Period and Cohort Effects Taken as a
Group.......................................212
7.6.2 HAPC Generalized Linear Mixed Models................215
7.7 Random Coefficients HAPC-CCREM..............................222
Appendix 7.1: Matrix Algebra Representations of Linear Mixed
Models and Generalized Linear Mixed Models................227
References.....................................................229
8 Mixed Effects Models: Hierarchical APC-Cross-Classified
Random Effects Models (HAPC-CCREM), Part II: Advanced
Analyses....................................................231
8.1 Introduction..............................................231
8.2 Level 2 Covariates: Age and Temporal Changes in Social
Inequalities in Happiness.................................231
8.3 HAPC-CCREM Analysis of Aggregate Rate Data on Cancer
Incidence and Mortality...................................243
8.3.1 Trends in Age, Period, and Cohort Variations:
Comparison with the IE Analysis.................. 243
8.3.2 Sex and Race Differentials.........................244
8.3.3 Cohort and Period Mechanisms: Cigarette Smoking,
Obesity, Hormone Replacement Therapy, and
Mammography...................................... 257
8.4 Full Bayesian Estimation..................................261
8.4.1 REML-EB Estimation.................................261
8.4.2 Gibbs Sampling and MCMC Estimation.................264
8.4.3 Discussion and Summary.............................268
8.5 HAPC-Variance Function Regression.........................269
8.5.1 Variance Function Regression: A Brief Overview....270
8.5.2 Research Topic: Changing Health Disparities........271
8.5.3 Intersecting the HAPC and VFR Models...............272
8.5.4 Results: Variations in Health and Health Disparities
by Age, Period, and Cohort, 1984-2007..............275
8.5.5 Summary.......................................... 280
References.....................................................282
9 Mixed Effects Models: Hierarchical APC-Growth Curve
Analysis of Prospective Cohort Data............................285
9.1 Introduction..............................................285
9.2 Intercohort Variations in Age Trajectories................287
9.2.1 Hypothesis.........................................287
9.2.2 Model Specification................................288
9.2.3 Results............................................291
9.3 Intracohort Heterogeneity in Age Trajectories.............294
9.3.1 Hypothesis.........................................294
9.3.2 Results........................................ 296
9.4 Intercohort Variations in Intracohort Heterogeneity Patterns ...300
9.4.1 Hypothesis.........................................300
9.4.2 Model Specification.............................. 301
9.4.3 Results............................................302
9.5 Summary.................................................307
References...................................................309
10 Directions for Future Research and Conclusion................313
10.1 Introduction............................................313
10.2 Additional Models.......................................315
10.2.1 The Smoothing Cohort Model and Nonparametric
Methods..........................................315
10.2.2 The Continuously Evolving Cohort Effects Model...316
10.3 Longitudinal Cohort Analysis of Balanced Cohort Designs
of Age Trajectories.....................................317
10.4 Conclusion..............................................319
References...................................................320
Index............................................................323
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.
|
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| id | DE-604.BV041481326 |
| illustrated | Not Illustrated |
| indexdate | 2024-12-20T16:48:24Z |
| institution | BVB |
| isbn | 9781466507524 |
| language | English |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-026927293 |
| oclc_num | 867173112 |
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| owner_facet | DE-19 DE-BY-UBM DE-473 DE-BY-UBG DE-706 DE-384 DE-11 DE-703 |
| physical | xiii, 338 Seiten Diagramme |
| psigel | ebook |
| publishDate | 2013 |
| publishDateSearch | 2013 |
| publishDateSort | 2013 |
| publisher | CRC Press, Taylor & Francis Group |
| record_format | marc |
| series2 | Interdisciplinary statistics |
| spellingShingle | Yang, Yang 1975- Land, Kenneth C. 1942- Age-period-cohort analysis new models, methods, and empirical applications Includes bibliographical references and index Cohort analysis Age groups / Statistical methods Altersgruppe (DE-588)4001469-1 gnd Kohortenanalyse (DE-588)4138256-0 gnd |
| subject_GND | (DE-588)4001469-1 (DE-588)4138256-0 |
| title | Age-period-cohort analysis new models, methods, and empirical applications |
| title_auth | Age-period-cohort analysis new models, methods, and empirical applications |
| title_exact_search | Age-period-cohort analysis new models, methods, and empirical applications |
| title_full | Age-period-cohort analysis new models, methods, and empirical applications Yang Yang and Kenneth C. Land |
| title_fullStr | Age-period-cohort analysis new models, methods, and empirical applications Yang Yang and Kenneth C. Land |
| title_full_unstemmed | Age-period-cohort analysis new models, methods, and empirical applications Yang Yang and Kenneth C. Land |
| title_short | Age-period-cohort analysis |
| title_sort | age period cohort analysis new models methods and empirical applications |
| title_sub | new models, methods, and empirical applications |
| topic | Cohort analysis Age groups / Statistical methods Altersgruppe (DE-588)4001469-1 gnd Kohortenanalyse (DE-588)4138256-0 gnd |
| topic_facet | Cohort analysis Age groups / Statistical methods Altersgruppe Kohortenanalyse |
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