Regression Calibration and Its Causal Variable Selection

Regression calibration is a statistical method used to correct for measurement error-caused bias when the analysis goal is to estimate the exposure-outcome association under a mismeasured continuous exposure. This talk will introduce two commonly used versions of regression calibration. It will then discuss, in a causal framework, how to include covariates in the calibration and outcome models for validity and efficiency. The talk will also introduce R functions that implement the regression calibration method (no required reading).

Presenter: Molin Wang, PhD