Comparative age-period-cohort analysis
Abstract 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 ch...
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| Format: | Article |
| Language: | English |
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BMC
2023-10-01
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| Series: | BMC Medical Research Methodology |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12874-023-02039-8 |
| _version_ | 1797452207258337280 |
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| author | Philip S. Rosenberg Adalberto Miranda-Filho David C. Whiteman |
| author_facet | Philip S. Rosenberg Adalberto Miranda-Filho David C. Whiteman |
| author_sort | Philip S. Rosenberg |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-09T15:05:26Z |
| format | Article |
| id | doaj.art-f263b84571cf4d27a842878149a44ae3 |
| institution | Directory Open Access Journal |
| issn | 1471-2288 |
| language | English |
| last_indexed | 2024-03-09T15:05:26Z |
| publishDate | 2023-10-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Research Methodology |
| spelling | doaj.art-f263b84571cf4d27a842878149a44ae32023-11-26T13:42:56ZengBMCBMC Medical Research Methodology1471-22882023-10-0123111310.1186/s12874-023-02039-8Comparative age-period-cohort analysisPhilip S. Rosenberg0Adalberto Miranda-Filho1David C. Whiteman2Division of Cancer Epidemiology and Genetics, Biostatistics Branch, National Cancer InstituteDivision of Cancer Epidemiology and Genetics, Biostatistics Branch, National Cancer InstituteCancer Control Group, QIMR Berghofer Medical Research InstituteAbstract 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.https://doi.org/10.1186/s12874-023-02039-8Age-period-cohort modelLexis diagramCancer surveillance researchSEER program |
| spellingShingle | Philip S. Rosenberg Adalberto Miranda-Filho David C. Whiteman Comparative age-period-cohort analysis BMC Medical Research Methodology Age-period-cohort model Lexis diagram Cancer surveillance research SEER program |
| 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 | Age-period-cohort model Lexis diagram Cancer surveillance research SEER program |
| url | https://doi.org/10.1186/s12874-023-02039-8 |
| work_keys_str_mv | AT philipsrosenberg comparativeageperiodcohortanalysis AT adalbertomirandafilho comparativeageperiodcohortanalysis AT davidcwhiteman comparativeageperiodcohortanalysis |