A Bayesian generalized age-period-cohort power model for cancer projections
JournalArticle (Originalarbeit in einer wissenschaftlichen Zeitschrift)
ID
2730122
Author(s)
Juergens, Verena; Ess, Silvia; Cerny, Thomas; Vounatsou, Penelope
Author(s) at UniBasel
Vounatsou, Penelope
Year
2014
Title
A Bayesian generalized age-period-cohort power model for cancer projections
Journal
Statistics in medicine
Volume
33
Number
26
Pages / Article-Number
4627-4636
Keywords
cancer projection, Bayesian inference, power model, Nordpred, lung cancer mortality
Abstract
Age-period-cohort (APC) models are the state of art in cancer projections, assessing past and recent trends and extrapolating mortality or incidence data into the future. Nordpred is a well-established software, assuming a Poisson distribution for the counts and a log-link or power-link function with fixed power; however, its predictive performance is poor for sparse data. Bayesian models with log-link function have been applied, but they can lead to extreme estimates. In this paper, we address criticisms of the aforementioned models by providing Bayesian formulations based on a power-link and develop a generalized APC power-link model, which assumes a random rather than fixed power parameter. In addition, a power model with a fixed power parameter of five was formulated in the Bayesian framework. The predictive performance of the new models was evaluated on Swiss lung cancer mortality data using model-based estimates of observed periods. Results indicated that the generalized APC power-link model provides best estimates for male and female lung cancer mortality. The gender-specific models were further applied to project lung cancer mortality in Switzerland during the periods 2009-2013 and 2014-2018. Copyright © 2014 John Wiley & Sons, Ltd.
Publisher
Wiley
ISSN/ISBN
0277-6715
edoc-URL
http://edoc.unibas.ch/dok/A6308502
Full Text on edoc
No
Digital Object Identifier DOI
10.1002/sim.6248
PubMed ID
http://www.ncbi.nlm.nih.gov/pubmed/24996118
ISI-Number
WOS:000345639700009
Document type (ISI)
Article
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