A Bayesian generalized age–period–cohort power model for cancer projections

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.