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Comparative age-period-cohort analysis
BACKGROUND: Cancer surveillance researchers analyze incidence or mortality rates jointly indexed by age group and calendar period using age-period-cohort models. Many studies consider age- and period-specific rates in two or more strata defined by sex, race/ethnicity, etc. A comprehensive characteri...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10585891/ https://www.ncbi.nlm.nih.gov/pubmed/37853346 http://dx.doi.org/10.1186/s12874-023-02039-8 |
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author | Rosenberg, Philip S. Miranda-Filho, Adalberto Whiteman, David C. |
author_facet | Rosenberg, Philip S. Miranda-Filho, Adalberto Whiteman, David C. |
author_sort | Rosenberg, Philip S. |
collection | PubMed |
description | BACKGROUND: Cancer surveillance researchers analyze incidence or mortality rates jointly indexed by age group and calendar period using age-period-cohort models. Many studies consider age- and period-specific rates in two or more strata defined by sex, race/ethnicity, etc. A comprehensive characterization of trends and patterns within each stratum can be obtained using age-period-cohort (APC) estimable functions (EF). However, currently available approaches for joint analysis and synthesis of EF are limited. METHODS: We develop a new method called Comparative Age-Period-Cohort Analysis to quantify similarities and differences of EF across strata. Comparative Analysis identifies whether the stratum-specific hazard rates are proportional by age, period, or cohort. RESULTS: Proportionality imposes natural constraints on the EF that can be exploited to gain efficiency and simplify the interpretation of the data. Comparative Analysis can also identify differences or diversity in proportional relationships between subsets of strata (“pattern heterogeneity”). We present three examples using cancer incidence from the United States Surveillance, Epidemiology, and End Results Program: non-malignant meningioma by sex; multiple myeloma among men stratified by race/ethnicity; and in situ melanoma by anatomic site among white women. CONCLUSIONS: For studies of cancer rates with from two through to around 10 strata, which covers many outstanding questions in cancer surveillance research, our new method provides a comprehensive, coherent, and reproducible approach for joint analysis and synthesis of age-period-cohort estimable functions. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-023-02039-8. |
format | Online Article Text |
id | pubmed-10585891 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-105858912023-10-20 Comparative age-period-cohort analysis Rosenberg, Philip S. Miranda-Filho, Adalberto Whiteman, David C. BMC Med Res Methodol Research BACKGROUND: Cancer surveillance researchers analyze incidence or mortality rates jointly indexed by age group and calendar period using age-period-cohort models. Many studies consider age- and period-specific rates in two or more strata defined by sex, race/ethnicity, etc. A comprehensive characterization of trends and patterns within each stratum can be obtained using age-period-cohort (APC) estimable functions (EF). However, currently available approaches for joint analysis and synthesis of EF are limited. METHODS: We develop a new method called Comparative Age-Period-Cohort Analysis to quantify similarities and differences of EF across strata. Comparative Analysis identifies whether the stratum-specific hazard rates are proportional by age, period, or cohort. RESULTS: Proportionality imposes natural constraints on the EF that can be exploited to gain efficiency and simplify the interpretation of the data. Comparative Analysis can also identify differences or diversity in proportional relationships between subsets of strata (“pattern heterogeneity”). We present three examples using cancer incidence from the United States Surveillance, Epidemiology, and End Results Program: non-malignant meningioma by sex; multiple myeloma among men stratified by race/ethnicity; and in situ melanoma by anatomic site among white women. CONCLUSIONS: For studies of cancer rates with from two through to around 10 strata, which covers many outstanding questions in cancer surveillance research, our new method provides a comprehensive, coherent, and reproducible approach for joint analysis and synthesis of age-period-cohort estimable functions. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12874-023-02039-8. BioMed Central 2023-10-18 /pmc/articles/PMC10585891/ /pubmed/37853346 http://dx.doi.org/10.1186/s12874-023-02039-8 Text en © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Rosenberg, Philip S. Miranda-Filho, Adalberto Whiteman, David C. Comparative age-period-cohort analysis |
title | Comparative age-period-cohort analysis |
title_full | Comparative age-period-cohort analysis |
title_fullStr | Comparative age-period-cohort analysis |
title_full_unstemmed | Comparative age-period-cohort analysis |
title_short | Comparative age-period-cohort analysis |
title_sort | comparative age-period-cohort analysis |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10585891/ https://www.ncbi.nlm.nih.gov/pubmed/37853346 http://dx.doi.org/10.1186/s12874-023-02039-8 |
work_keys_str_mv | AT rosenbergphilips comparativeageperiodcohortanalysis AT mirandafilhoadalberto comparativeageperiodcohortanalysis AT whitemandavidc comparativeageperiodcohortanalysis |