Abstract
A methodology is presented which utilizes multiple sources of data to estimate the transition parameters for a model of respiratory cancer morbidity and mortality. This methodology employs auxiliary evidence from clinical and epidemiological studies to structure a stochastic compartment model which makes it possible to infer unobserved morbidity transition rates from a population-based mortality time series for selected cohorts. By expressing a double convolution equation as a sum of a finite number of terms, it is possible to calculate maximum likelihood estimates (MLEs) of parameters representing transitions through three stages in the natural history of the disease process. Calculations are illustrated using national mortality data for nine U.S. white male cohorts followed through the period 1950–1977.
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