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Predicting Confidence Intervals for the Age-Period-Cohort Model

Forecasting incidence and/or mortality rates of cancer is of special interest to epidemiologists, health researchers and other planners in predicting the demand for health care. This paper proposes a methodology for developing prediction intervals using forecasts from Poisson APC models. The annual Canadian age-specific prostate cancer mortality rates among males aged 45 years or older for the period between 1950 and 1990 are calculated using 5-year intervals. The data were analyzed by fitting an APC model to the logarithm of the mortality rate. Based on the fit of the 1950 to 1979 data, the known prostate mortality in 1980 to 1990 is estimated. The period effects, for 1970-1979, are extended linearly to estimate the next ten period effects. With the aims of parsimony, scientific validity, and a reasonable fit to existing data two different possible forms are evaluated namely, the ageperiod and the age-period-cohort models. The asymptotic 95% prediction intervals are based on the standard errors using an assumption of normality (estimate ±1.96× standard error of the estimate).