Accounting for measurement error to assess the effect of air pollution on omic signals.

ABSTRACT: Studies on the effects of air pollution and more generally environmental exposures on health require measurements of pollutants, which are affected by measurement error. This is a cause of bias in the estimation of parameters relevant to the study and can lead to inaccurate conclusions when evaluating associations among pollutants, disease risk and biomarkers. Although the presence of measurement error in such studies has been recognized as a potential problem, it is rarely considered in applications and practical solutions are still lacking. In this work, we formulate Bayesian measurement error models and apply them to study the link between air pollution and omic signals. The data we use stem from the "Oxford Street II Study", a randomized crossover trial in which 60 volunteers walked fo

SUBMITTER: Ponzi E 

PROVIDER: S-EPMC6940143 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature