Age-Period-Cohort Models in Cancer Surveillance Research: Ready for Prime Time?

Authors' Affiliation: National Cancer Institute, Division of Cancer Epidemiology and Genetics, Bethesda, Maryland.
Cancer Epidemiology Biomarkers & Prevention (Impact Factor: 4.32). 06/2011; 20(7):1263-8. DOI: 10.1158/1055-9965.EPI-11-0421
Source: PubMed

ABSTRACT Standard descriptive methods for the analysis of cancer surveillance data include canonical plots based on the lexis diagram, directly age-standardized rates (ASR), estimated annual percentage change (EAPC), and joinpoint regression. The age-period-cohort (APC) model has been used less often. Here, we argue that it merits much broader use. First, we describe close connections between estimable functions of the model parameters and standard quantities such as the ASR, EAPC, and joinpoints. Estimable functions have the added value of being fully adjusted for period and cohort effects, and generally more precise. Second, the APC model provides the descriptive epidemiologist with powerful new tools, including rigorous statistical methods for comparative analyses, and the ability to project the future burden of cancer. We illustrate these principles by using invasive female breast cancer incidence in the United States, but these concepts apply equally well to other cancer sites for incidence or mortality.

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Available from: William F Anderson, Sep 09, 2014
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    • "The EAPC can be estimated through an age-drift model, and we can easily build a confidence interval for the EAPC using the standard error of the drift [13]. The age-drift model is a simplified version of the age-cohort model where the cohort effect is parameterized using only the linear component. "
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