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Age-period-cohort models using smoothing splines: a generalized additive model approach.
Bei Jiang1, Keumhee C Carriere
1Department of Mathematical and Statistical Sciences, University of Alberta, Edmonton, AB, Canada, T6G 2G1.
Statistics in Medicine
|September 10, 2013
Summary
Cubic smoothing splines effectively address limitations in age-period-cohort (APC) models for analyzing sparse disease or mortality data. This method enhances the analysis of temporal trends, even with incomplete datasets from a Lexis diagram.
Area of Science:
- Epidemiology
- Biostatistics
- Statistical Modeling
Background:
- Age-period-cohort (APC) models analyze disease/mortality trends but face limitations due to data sparseness and linear dependencies.
- Existing APC models struggle with the inherent non-identifiability and practical data limitations.
Purpose of the Study:
- To propose and evaluate cubic smoothing splines as a solution for age-period-cohort modeling with sparse data.
- To address the non-identifiability issue in APC models using generalized additive models.
Main Methods:
- Utilized cubic smoothing splines within a generalized additive model framework.
- Applied methods of estimable functions to resolve non-identifiability problems.
- Conducted simulation studies to assess performance via mean squared errors.
Main Results:
- Cubic smoothing splines demonstrate effectiveness in handling sparse, unaggregated data from Lexis diagrams.
- The proposed method successfully addresses the non-identifiability issue in APC modeling.
- Simulation studies confirmed the robust performance of cubic smoothing splines.
Conclusions:
- Cubic smoothing splines offer a viable and effective approach for age-period-cohort modeling with sparse data.
- This methodology enhances the analysis of temporal disease and mortality trends.
- The findings support the application of cubic smoothing splines for epidemiological and biostatistical research.
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