Issues in Implementing Regression Calibration Analyses.

ABSTRACT: Regression calibration is a popular approach for correcting biases in estimated regression parameters when exposure variables are measured with error. This approach involves building a calibration equation to estimate the value of the unknown true exposure given the error-prone measurement and other covariates. The estimated, or calibrated, exposure is then substituted for the unknown true exposure in the health outcome regression model. When used properly, regression calibration can greatly reduce the bias induced by exposure measurement error. Here, we first provide an overview of the statistical framework for regression calibration, specifically discussing how a special type of error, called Berkson error, arises in the estimated exposure. We then present practical issues to consider wh

SUBMITTER: Boe LA 

PROVIDER: S-EPMC10666971 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature