Abstract
Forecasting incidence and/or mortality rates of cancer is of special inter est to epidemiologists, health researchers and other planners in predicting the demand for health care. This paper proposes a methodology for devel oping 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 age period 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)
Highlights
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 (Schaubel et al 1998)
The APC model appears suitable for forecasting the mortality due to prostate cancer
It has been shown that projections based on APC models can be uniquely determined and are not affected by the identifiability problem (Holford 1985)
Summary
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 (Schaubel et al 1998). Poisson distribution for the number of incident cases, but such linear trends are not likely to last indefinitely and both the year in which the disease was diagnosed (period); and the year in which the subject was born (cohort) may contribute simultaneously to the observed rates of cancer incidence and mortality. This has led to the increasing use of log-linear Poisson age-period-cohort (APC) models for the statistical analysis of this type of data (Osmond 1985). Our specific objective here is to develop prediction intervals using forecasts from Poisson APC models
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