Recent developments in the multistage modeling of cohort data for carcinogenic risk assessment

Abstract

The modeling of cohort data based on the Armitage-Doll multistage model of the carcinogenic process has gained popular acceptance as a methodology for quantitative risk assessment for estimating the dose-related relationships between different occupational and environmental carcinogenic exposures and cancer mortality. The multistage model can be used for extrapolation to low doses relevant for setting environmental standards and also provides information regarding whether more than one stage is dose-related, which assists in determining whether different carcinogens affect different stages of the cancer process. This paper summarizes recent developments in the multistage modeling of cohort data and emphasizes practical issues such as handling data arising from large epidemiologic follow-up studies, the time-dependent nature of exposures and statistical issues such as multicollinearity in time-related variables, robustness of parameter estimates, validating of the fitted models, and routine inferential procedures. Problems related to uncertainties of risk estimates are discussed also. Computer programs for fitting multistage models with one or two dose-related stages to cohort data incorporating time-dependent exposure patterns; constructing confidence regions for model parameters; and predicting lifetime risks of dying from cancer adjusting for competing causes of death are detailed. Illustrations are provided using lung cancer mortality in a cohort of nonwhite male coke oven workers exposed to coal tar pitch volatiles.