W C Lee1, R S Lin. 1. Institute of Epidemiology, College of Public Health, National Taiwan University, Taipei, Republic of China.
Abstract
BACKGROUND: Recently the age-period-cohort (APC) model has become a popular epidemiological tool. However, it is well known that the model suffers from the identifiability problem. The simple multiplicative formulation of the model in terms of the age, period, and cohort variables without resorting to the underlying biology also casts doubt on the interpretability of the model parameters. METHODS: Excess risk APC models for cancers are developed based on carcinogenesis processes in human populations. These models have the beneficial feature of biological plausibility and do not suffer from the identifiability problem. Apart from the age, period, and cohort effects, a new kind of effect, the impact effect, is also introduced into the models. A computer program has been developed to fit the models which contain non-linear as well as restricted parameters. RESULTS: Two published mortality datasets are used to demonstrate the methodology. The proposed models fit better than the conventional APC model in both examples. CONCLUSIONS: Despite all the merits of the proposed models, several statistical issues should be investigated further before accepting this methodology as a general data-analytical tool.
BACKGROUND: Recently the age-period-cohort (APC) model has become a popular epidemiological tool. However, it is well known that the model suffers from the identifiability problem. The simple multiplicative formulation of the model in terms of the age, period, and cohort variables without resorting to the underlying biology also casts doubt on the interpretability of the model parameters. METHODS: Excess risk APC models for cancers are developed based on carcinogenesis processes in human populations. These models have the beneficial feature of biological plausibility and do not suffer from the identifiability problem. Apart from the age, period, and cohort effects, a new kind of effect, the impact effect, is also introduced into the models. A computer program has been developed to fit the models which contain non-linear as well as restricted parameters. RESULTS: Two published mortality datasets are used to demonstrate the methodology. The proposed models fit better than the conventional APC model in both examples. CONCLUSIONS: Despite all the merits of the proposed models, several statistical issues should be investigated further before accepting this methodology as a general data-analytical tool.