Risk Estimation with Epidemiologic Data When Response Attenuates at High-Exposure Levels | Zendy

Kyle Steenland | Zendy; Ryan Seals | Zendy; Mitch Klein | Zendy; Jennifer Jinot | Zendy; Henry D. Kahn | Zendy

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Risk Estimation with Epidemiologic Data When Response Attenuates at High-Exposure Levels

Author(s) -

Kyle Steenland,

Ryan Seals,

Mitch Klein,

Jennifer Jinot,

Henry D. Kahn

Publication year - 2011

Publication title -

environmental health perspectives

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 2.257

H-Index - 282

eISSN - 1552-9924

pISSN - 0091-6765

DOI - 10.1289/ehp.1002521

Subject(s) - linear model , log linear model , linear regression , statistics , exposure assessment , linear relationship , mathematics , parametric statistics

In occupational studies, which are commonly used for risk assessment for environmental settings, estimated exposure-response relationships often attenuate at high exposures. Relative risk (RR) models with transformed (e.g., log- or square root-transformed) exposures can provide a good fit to such data, but resulting exposure-response curves that are supralinear in the low-dose region may overestimate low-dose risks. Conversely, a model of untransformed (linear) exposure may underestimate risks attributable to exposures in the low-dose region.

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