title: Analysing the temporal effects of age, period and cohort.
abstract: Longitudinal trends can be analysed in terms of the effect of age, birth cohort or year of diagnosis. All three temporal effects are thought to be useful by epidemiologists, but they are not identifiable when assessed simultaneously. Partitioning the effects in terms of linear and curvature components is one approach to understanding the problem and finding a reasonable summary of trends. Other solutions can be expressed in terms of these components, and they can also be used to understand both subgroup and temporal interactions. One approach that may offer a way of understanding the effect of risk factor trends on population based rates is to use models that incorporate an effect due to the risk factors. These methods are discussed using lung cancer incidence and mortality to illustrate the underlying concepts.
authors.affiliation: Department of Epidemiology and Public Health, Yale University Medical School, New Haven, CT 06510.
authors.forename: T R
authors.initials: TR
authors.lastname: Holford
mesh_terms: D000328:Adult; D000367:Age Factors; D015331:Cohort Studies; D003237:Connecticut / Q000453:epidemiology; D005260:Female; D006801:Humans; D015994:Incidence; D016014:Linear Models; D008137:Longitudinal Studies; D008175:Lung Neoplasms / Q000453:epidemiology / Q000401:mortality; D008297:Male; D015233:Models, Statistical; D012307:Risk Factors; D016019:Survival Analysis; D013997:Time Factors