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Lung cancer rate predictions using generalized additive models

Clements, Mark; Armstrong, Bruce K; Moolgavkar, Suresh


Predictions of lung cancer incidence and mortality are necessary for planning public health programs and clinical services. It is proposed that generalized additive models (GAMs) are practical for cancer rate prediction. Smooth equivalents for classical age-period, age-cohort, and age-period-cohort models are available using one-dimensional smoothing splines. We also propose using two-dimensional smoothing splines for age and period. Variance estimation can be based on the bootstrap. To assess...[Show more]

CollectionsANU Research Publications
Date published: 2005
Type: Journal article
Source: Biostatistics
DOI: 10.1093/biostatistics/kxi028


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