title: Estimating age, period and cohort effects using the multistage model for cancer.
abstract: To understand cancer aetiology better, epidemiologists often try to investigate the time trends in disease incidence with year of diagnosis (period) and birth cohort. Unfortunately, one cannot identify these factors uniquely in the usual regression model owing to a linear dependence between age, period and cohort, so that one requires additional information 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 multistage carcinogenesis model and its extensions to obtain a unique set of parameters for an age-period-cohort model of lung cancer trends of Connecticut males and females from 1935 to 1988. Some of these models 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.
authors.affiliation: Department of Epidemiology and Public Health, Yale University Medical School, New Haven, CT 06510, USA.
authors.forename: T R
authors.initials: TR
authors.lastname: Holford
authors.forename: Z
authors.initials: Z
authors.lastname: Zhang
authors.forename: L A
authors.initials: LA
authors.lastname: McKay
mesh_terms: D000367:Age Factors; D002471:Cell Transformation, Neoplastic; D015988:Cohort Effect; D003237:Connecticut / Q000453:epidemiology; D005260:Female; D006801:Humans; D015994:Incidence; D016013:Likelihood Functions; D008175:Lung Neoplasms / Q000453:epidemiology; D008297:Male; D008954:Models, Biological; D015233:Models, Statistical; D009369:Neoplasms / Q000453:epidemiology*; D012307:Risk Factors; D013269:Stochastic Processes; D013997:Time Factors