On measurement error adjustment methods in Poisson regression

Abstract

This paper echoes the familiar warning that additive measurement error and multicollinearity in the explanatory variables can mislead investigators in multiple Poisson regression analysis. Often, a causal variable measured with error may be overlooked and its significance transferred to another covariate. Two measurement error adjustment methods were applied and compared in terms of mean squared error of the regression estimates as well as confidence interval coverage of the parameters. Computer simulation was used to evaluate these methods when the explanatory variables are subject to both classical and Berkson error. Results showed that the regression calibration method (RCAL) performed the best in all situations considered, except in the presence of Berkson error when the predictor variables are highly correlated. Copyright © 1999 John Wiley & Sons, Ltd.