Measurement Errors in Environmental Epidemiology

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Principal Investigator:
Donna Spiegelman, Professor of Epidemiologic Methods



Dates of Research:
May 1, 2009 — February 28, 2013


Exposure measurement error is a likely source of bias in nearly all environmental and epidemiological studies, typically leading to under-estimation of relative risks and loss of statistical power to detect effects. In the previous cycle of this project, the regression calibration method for adjustment for measurement error in multivariate regression models was extended, including Cox models and logistic models, to accommodate the study designs and data structures encountered in the environmental epidemiology. The study will focus on issues of air pollution epidemiology- in particular the chronic effects of particulate exposure to all NO2 on all-cause mortality, cardio-vascular mortality, and lung cancer mortality.