DOSE RESPONSE MODELING IN EPIDEMIOLOGIC COHORT STUDIES

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This proposal addresses the problem of nonlinear dose response estimation in environmental and occupational cohort studies by exploring two more flexible regression strategies: Generalized Additive Models (non-parametric regression) and a nonlinear dose metric. Typically, dose response models assume that the relationship is linear on some scale. Many disease mechanisms, however, such as sensitization or carcinogenesis, may produce nonlinearities in the dose-response curve. Moreover, linear models may be inappropriate in occupational cohort studies where the healthy worker effect can lead to an apparent plateau or even downturn in risk among the more highly exposed. General additive models will be used to describe the shapes of the dose-response curve between cumulative exposures and selected outcomes in three cohort mortality studies with well established exposure response associations. The three data sets available for dose-response modeling are: 46,400 autoworkers exposed to metalworking fluids, 5,414 Vermont granite workers exposed to silica in quartz form and 2,342 diatomaceous earth miners exposed to crystalline silica in cristobalite form. Disease outcomes of interest will include cancers of the stomach, esophagus, pancreas, and liver in the metalworking fluid cohort, and cancer of the lung in the two silica cohorts. Nonmalignant respiratory disease mortality will be examined in all cohorts. In addition, we will apply a flexible dose model for metalworking fluids and crystalline silica that includes simple cumulative exposure as a special case. Unlike standard analyses that are limited to linear relations with cumulative exposure, this model, proposed by Seixas, is sufficiently flexible to enable investigation of nonlinear dose-rate effects and variable disease induction/latency intervals. Secondary objectives include the direct comparisons of the carcinogenicity of the four types of metalworking fluids (mineral oil, solubles, synthetic, and semi- synthetics) and of quartz and cristobalite polymorphs of crystalline silica.