Causal Selection of Covariates in Regression Calibration for Mismeasured Continuous Exposure.

ABSTRACT: Regression calibration as developed by Rosner, Spiegelman, and Willett is used to adjust the bias in effect estimates due to measurement error in continuous exposures. The method involves two models: a measurement error model relating the mismeasured exposure to the true (or gold-standard) exposure and an outcome model relating the mismeasured exposure to the outcome. However, no comprehensive guidance exists for determining which covariates should be included in each model. In this article, we investigate the selection of the minimal and most efficient covariate adjustment sets under a causal inference framework. We show that to address the measurement error, researchers must adjust for, in both measurement error and outcome models, any common causes (1) of true exposure and the outcome a

SUBMITTER: Tang W 

PROVIDER: S-EPMC12337126 | biostudies-literature | 2024 May

REPOSITORIES: biostudies-literature