Model-averaged confounder adjustment for estimating multivariate exposure effects with linear regression.

ABSTRACT: In environmental and nutritional epidemiology and in many other fields, there is increasing interest in estimating the effect of simultaneous exposure to several agents (e.g., multiple nutrients, pesticides, or air pollutants) on a health outcome. We consider estimating the effect of a multivariate exposure that includes several continuous agents and their interactions-on an outcome, when the true confounding variables are an unknown subset of a potentially large (relative to sample size) set of measured covariates. Our approach is rooted in the ideas of Bayesian model averaging: the exposure effect is estimated as a weighted average of the estimated exposure effects obtained under several linear regression models that include different sets of the potential confounders. We introduce a dat

SUBMITTER: Wilson A 

PROVIDER: S-EPMC6518418 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature