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Estimating age, period and cohort effects using the multistage model for cancer
Author(s) -
Holford Theodore R.,
Zhang Zhongxin,
McKay Lisa A.
Publication year - 1994
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.4780130105
Subject(s) - cohort , cohort effect , statistics , set (abstract data type) , cancer , demography , regression analysis , incidence (geometry) , econometrics , medicine , mathematics , computer science , geometry , sociology , programming language
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.