Prognosis of Lung Cancer Mortality in West Germany: A Case Study in Bayesian Prediction. (REVISED, January 2000)

We apply a generalized Bayesian age-period-cohort (APC) model to a
dataset on lung cancer mortality in West Germany, 1952-1996. Our
goal is to predict future deaths rates until the year 2010,
separately for males and females. Since age and period is not
measured on the same grid, we propose a generalized APC-model where
consecutive cohort parameters represent strongly overlapping birth
cohorts. This approach results in a rather large number of
parameters, where standard algorithms for statistical inference by
Markov chain Monte Carlo (MCMC) methods turn out to be
computationally intensive. We propose a more efficient
implementation based on ideas of block sampling from the time
series literature. We entertain two different formulations,
penalizing either first or second differences of age, period and
cohort parameters. To assess the predictive quality of both
formulations, we first forecast the rates for the period 1987-1996
based on data until 1986. A comparison with the actual observed
rates is made based on quantities related to the predictive
deviance. Predictions of lung cancer mortality until 2010 both for
males and females are finally reported.