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