An application of hierarchical regression in the investigation of multiple paternal occupational exposures and neuroblastoma in offspring.

BACKGROUND: We used hierarchical regression to study the effects of 46 paternal occupational exposures on the incidence of neuroblastoma in offspring.
METHODS: The study population included 405 cases and 302 controls. The effect of each exposure was estimated using both conventional maximum likelihood and hierarchical regression.
RESULTS: Using hierarchical regression, overall precision was greatly enhanced compared to the conventional analysis. In addition, adjustment of effect estimates based on prespecified prior distributions of the true effect parameters allowed a more consistent interpretation across the entire panel of exposures. Estimates for several metals and solvents were shrunk close to the null value, whereas estimates for several thinner solvents, diesel fuel, solders, wood dust, and grain dust remained moderately elevated.
CONCLUSIONS: Hierarchical regression may mitigate some of the problems of the conventional approach by controlling for correlated exposures, enhancing the precision of estimates, and providing some adjustment of estimates based on prior knowledge.