Prediction of cancer incidence in the Nordic countries: empirical comparison of different approaches

Prediction of the future number of cancer cases is of great interest to society. The classical approach is to use the age‐period‐cohort model for making cancer incidence predictions. We made an empirical comparison of different versions of this model, using data from cancer registries in the Nordic...

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Bibliographic Details
Published in:Statistics in medicine 2003-09, Vol.22 (17), p.2751-2766
Main Authors: Møller, Bjørn, Fekjær, Harald, Hakulinen, Timo, Sigvaldason, Helgi, Storm, Hans H., Talbäck, Mats, Haldorsen, Tor
Format: Article
Language:English
Subjects:
age-period-cohort model
Biological and medical sciences
cancer incidence
Cohort Studies
Empirical Research
evaluation
forecast
Forecasting - methods
Humans
Incidence
Medical sciences
Models, Statistical
Neoplasms - classification
Neoplasms - epidemiology
prediction
projection
Scandinavian and Nordic Countries - epidemiology
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Summary:Prediction of the future number of cancer cases is of great interest to society. The classical approach is to use the age‐period‐cohort model for making cancer incidence predictions. We made an empirical comparison of different versions of this model, using data from cancer registries in the Nordic countries for the period 1958–1997. We have applied 15 different methods to 20 sites for each sex in Denmark, Finland, Norway and Sweden. Median absolute value of the relative difference between observed and predicted numbers of cases for these 160 combinations of site, sex and country was calculated. The medians varied between 10.4 per cent and 15.3 per cent in predictions 10 years ahead, and between 15.1 per cent and 32.0 per cent for 20 year predictions. We have four main conclusions: (i) projecting current trends worked better than assuming that future rates are equal to present rates; (ii) the method based on the multiplicative APC model often overestimated the number of cancer cases due to its exponential growth over time, but using a power function to level off this growth improved the predictions; (iii) projecting only half of the trend after the first 10 years also gave better long‐term predictions; (iv) methods that emphasize trends in the last decade seem to perform better than those that include earlier time trends. Copyright © 2003 John Wiley & Sons, Ltd.
ISSN:0277-6715
1097-0258
DOI:10.1002/sim.1481