title: Age-period-cohort models using smoothing splines: a generalized additive model approach.
abstract: Age-period-cohort (APC) models are used to analyze temporal trends in disease or mortality rates, dealing with linear dependency among associated effects of age, period, and cohort. However, the nature of sparseness in such data has severely limited the use of APC models. To deal with these practical limitations and issues, we advocate cubic smoothing splines. We show that the methods of estimable functions proposed in the framework of generalized linear models can still be considered to solve the non-identifiability problem when the model fitting is within the framework of generalized additive models with cubic smoothing splines. Through simulation studies, we evaluate the performance of the cubic smoothing splines in terms of the mean squared errors of estimable functions. Our results support the use of cubic smoothing splines for APC modeling with sparse but unaggregated data from a Lexis diagram.
authors.affiliation: Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB, Canada, T6G 2G1.
authors.forename: Bei
authors.initials: B
authors.lastname: Jiang
authors.forename: Keumhee C
authors.initials: KC
authors.lastname: Carriere
mesh_terms: D000367:Age Factors; D015331:Cohort Studies; D003198:Computer Simulation; D006801:Humans; D008654:Mesothelioma / Q000453:epidemiology; D015233:Models, Statistical; D013997:Time Factors
keywords: Age-period-cohort models; estimable functions; generalized additive models; smoothing splines