ESTIMATING AGE, PERIOD AND COHORT EFFECTS USING THE MULTISTAGE MODEL FOR CANCER

To understand cancer aetiology better, epidemiologists often try to in vestigate the time trends in disease incidence with year of diagnosis (period) and birth cohort. Unfortunately, one cannot identify these fa ctors uniquely in the usual regression model owing to a linear depende nce between age, period and cohort, so that one requires additional in formation about the underlying biology of the disease. Carcinogenesis models provide one type of information that can result in a unique set of parameters for the effects of age, period and cohort. We use the m ultistage carcinogenesis model and its extensions to obtain a unique s et of parameters for an age-period-cohort model of lung cancer trends of Connecticut males and females from 1935 to 1988. Some of these mode ls do not seem to provide a reasonable set of model parameters, but we found that a model that included second-order terms and a multistage mixture model both gave a good fit to the data and realistic parameter estimates.