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Biostatistics Advance Access published online on April 28, 2005
Biostatistics, doi:10.1093/biostatistics/kxi028
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© The Author 2005. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oupjournals.org.
Received August 10, 2004
Revised March 25, 2005
Accepted April 25, 2005

Article

Lung cancer rate predictions using generalized additive models

Mark S. Clements 1*, Bruce K. Armstrong 2, and Suresh H. Moolgavkar 3

1 National Centre for Epidemiology and Population Health, The Australian National University, Canberra, ACT 0200, Australia
2 School of Public Health, The University of Sydney, Sydney, Australia
3 Fred Hutchinson Cancer Research Center, Seattle, Washington, U.S.A.

* To whom correspondence should be addressed.
Mark S. Clements, E-mail: Mark.Clements{at}anu.edu.au


  Abstract

Predictions of lung cancer incidence and mortality are necessary for planning public health programs and clinical services. It is proposed that generalized additive models 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 predictive performance, we compared the models with a Bayesian age-period-cohort model. Model comparison used cross-validation and measures of predictive performance for recent predictions. The models were applied to data from the WHO Mortality Database for females in five countries. Model choice between the age-period-cohort models and two-dimensional models was equivocal with respect to cross-validation, while the two-dimensional generalized additive models had very good predictive performance. The Bayesian model performed poorly due to imprecise predictions and the assumption of linearity outside of observed data.

In summary, the two-dimensional generalized additive model performed well. The generalized additive models make the important prediction that female lung cancer rates in these countries will be stable or begin to decline in the future.

Keywords: age-period-cohort model; trends; lung cancer; Bayesian.
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