ArticlePloS one2020
Clarifying assumptions in age-period-cohort analyses and validating results.
Article in PloS one, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Trends in obesity-related cardiovascular and cancer mortality in Switzerland 1995-2019: an analysis of multiple causes of death.American journal of epidemiology · 2026Article
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- Evaluation of deep vein thrombosis prophylaxis use in a Northwest Ethiopian medical ward: an observational follow-up study.Frontiers in medicine · 2025Article
- Age-specific determinants of reduced exercise capacity in youth after heart transplant: A longitudinal cohort study.JHLT open · 2024Article
- Age-period-cohort analysis of smoking prevalence trends among career military personnel in South Korea.Tobacco induced diseases · 2024Article
- Re: Non-progressive breast carcinomas detected at mammography screening: a population study-a model test or a novel test of cancer regression?Breast cancer research : BCR · 2023Article
- Trends in cannabis use intention around the period of cannabis legalisation in Australia: An age-period-cohort model.Drug and alcohol review · 2023Article
- Age-period-cohort analysis of depression trends: are depressive symptoms increasing across generations in Germany?European journal of ageing · 2022Article
- Gender and tobacco epidemic in South Korea: implications from age-period-cohort analysis and the DPSEEA framework.BMJ open · 2022Article
- Trends in "Deaths of Despair" Among Working-Aged White and Black Americans, 1990-2017.American journal of epidemiology · 2021Article
- Age Patterns in Self-Reported Cognitive Impairment Among Older Latino Subgroups and Non-Latino Whites in the United States, 1997-2018: Implications for Public Health Policy.Innovation in aging · 2021Article
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2 authors.
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Abstract
backgroundAge-period-cohort (APC) models are often used to decompose health trends into period- and cohort-based sources, but their use in epidemiology and population sciences remains contentious. Central to the contention are researchers' failures to 1) clearly state their analytic assumptions and/or 2) thoroughly evaluate model results. These failures often produce varying conclusions across APC studies and generate confusion about APC methods. Consequently, scholarly exchanges about APC methods usually result in strong disagreements that rarely offer practical advice to users or readers of APC methods.
methodsWe use research guidelines to help practitioners of APC methods articulate their analytic assumptions and validate their results. To demonstrate the usefulness of the guidelines, we apply them to a 2015 American Journal of Epidemiology study about trends in black-white differences in U.S. heart disease mortality.
resultsThe application of the guidelines highlights two important findings. On the one hand, some APC methods produce inconsistent results that are highly sensitive to researcher manipulation. On the other hand, other APC methods estimate results that are robust to researcher manipulation and consistent across APC models.
conclusionsThe exercise shows the simplicity and effectiveness of the guidelines in resolving disagreements over APC results. The cautious use of APC models can generate results that are consistent across methods and robust to researcher manipulation. If followed, the guidelines can likely reduce the chance of publishing variable and conflicting results across APC studies.
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