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Lung cancer rate predictions using generalized additive models.

Mark S Clements1, Bruce K Armstrong, Suresh H Moolgavkar

  • 1National Centre for Epidemiology and Population Health, The Australian National University, Canberra, ACT 0200, Australia. Mark.Clements@anu.edu.au

Biostatistics (Oxford, England)
|April 30, 2005
PubMed
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Generalized additive models (GAMs) offer practical cancer rate prediction. Two-dimensional GAMs demonstrated strong predictive performance, forecasting stable or declining female lung cancer rates.

Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Accurate lung cancer incidence and mortality predictions are crucial for public health planning and clinical services.
  • Generalized additive models (GAMs) are proposed as a practical approach for cancer rate prediction.

Purpose of the Study:

  • To evaluate the utility of generalized additive models (GAMs) for predicting lung cancer rates.
  • To compare the predictive performance of two-dimensional GAMs with traditional age-period-cohort models and a Bayesian approach.

Main Methods:

  • Utilized one-dimensional and two-dimensional smoothing splines within GAMs for age-period and age-period-cohort modeling.
  • Employed bootstrap methods for variance estimation.
  • Assessed predictive performance using cross-validation and recent prediction measures, comparing GAMs against a Bayesian age-period-cohort model.

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Main Results:

  • Two-dimensional GAMs exhibited very good predictive performance.
  • Model selection between age-period-cohort models and two-dimensional models was equivocal based on cross-validation.
  • The Bayesian model showed poor performance due to imprecise predictions and linearity assumptions.

Conclusions:

  • Two-dimensional GAMs are a well-performing method for cancer rate prediction.
  • GAMs predict a future stabilization or decline in female lung cancer rates in the studied countries.