Measurement error models with interactions Academic Article uri icon

abstract

  • An important use of measurement error models is to correct regression models for bias due to covariate measurement error. Most measurement error models assume that the observed error-prone covariate (WW ) is a linear function of the unobserved true covariate (X) plus other covariates (Z) in the regression model. In this paper, we consider models for W that include interactions between X and Z. We derive the conditional distribution of X given W and Z and use it to extend the method of regression calibration to this class of measurement error models. We apply the model to dietary data and test whether self-reported dietary intake includes an interaction between true intake and body mass index. We also perform simulations to compare the model to simpler approximate calibration models.

altmetric score

  • 1

author list (cited authors)

  • Midthune, D., Carroll, R. J., Freedman, L. S., & Kipnis, V.

citation count

  • 5

publication date

  • April 2016