title: Lung cancer rate predictions using generalized additive models.
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 (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 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 World Health Organization Mortality Database for females in five countries. Model choice between the age-period-cohort models and the two-dimensional models was equivocal with respect to cross-validation, while the two-dimensional GAMs 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 GAM performed well. The GAMs make the important prediction that female lung cancer rates in these countries will be stable or begin to decline in the future.
authors.affiliation: National Centre for Epidemiology and Population Health, The Australian National University, Canberra, ACT 0200, Australia. Mark.Clements@anu.edu.au
authors.forename: Mark S
authors.initials: MS
authors.lastname: Clements
authors.forename: Bruce K
authors.initials: BK
authors.lastname: Armstrong
authors.forename: Suresh H
authors.initials: SH
authors.lastname: Moolgavkar
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D001499:Bayes Theorem; D005260:Female; D005544:Forecasting / Q000379:methods*; D006801:Humans; D008175:Lung Neoplasms / Q000453:epidemiology*; D008875:Middle Aged; D015233:Models, Statistical; D013997:Time Factors