An empirical Bayes formulation of cohort models in cancer epidemiology.
This paper concerns the incidence rates of malignant skin melanoma for several age-sex groups and time periods in three geographic regions, uses a method of cohort analysis and employs a two-stage random effects model. The first stage entails the assumption that the within-region variation in the frequency of disease incidence for a fixed age-sex-cohort group has a Poisson distribution with mean proportional to the population at risk. The second stage, after adjusting for age and sex, entails the assumption that the between-region geographic variation in the logarithm of the true incidence rate has a prior distribution with parameters estimated by the method of maximum likelihood. After adjusting for age effects, we estimate random geographic-specific cohort effects for each sex with use of an empirical Bayes method and compare the results with the usual multiplicative Poisson model that assumes fixed geographic-specific cohort effects for each sex. This comparison shows that the method presented here provides more stable estimates of geographic-specific cohort effects, and in addition the random effects model describes these data more adequately.
Department of Mathematical Sciences, University of North Carolina, Wilmington 28403-3297.
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D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001499:Bayes Theorem; D015331:Cohort Studies; D003237:Connecticut / Q000453:epidemiology; D003718:Denmark / Q000453:epidemiology; D005260:Female; D006801:Humans; D015994:Incidence; D008297:Male; D008545:Melanoma / Q000453:epidemiology*; D008875:Middle Aged; D015233:Models, Statistical; D009664:Norway / Q000453:epidemiology; D016012:Poisson Distribution; D012878:Skin Neoplasms / Q000453:epidemiology*