Measurement error models with interactions. Academic Article

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.

authors

• Carroll, Raymond

published proceedings

• Biostatistics

altmetric score

• 0.5

author list (cited authors)

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

citation count

• 6

complete list of authors

• Midthune, Douglas||Carroll, Raymond J||Freedman, Laurence S||Kipnis, Victor

publication date

• April 2016

publisher

• Oxford University Press (OUP)  Publisher

published in

• Biostatistics  Journal

keywords

• Adult
• Body Mass Index
• Calibration
• Computer Simulation
• Diet
• Female
• Humans
• Interactions
• Male
• Measurement Error
• Mixed Models
• Models, Statistical
• Nonlinear Mixed Models
• Nutritional Epidemiology
• Regression Analysis
• Research Design

PubMed Central ID

• 26530858

Digital Object Identifier (DOI)

• 10.1093/biostatistics/kxv043

start page

• 277

end page

• 290

volume

• 17

issue

• 2

URL

• http%3A%2F%2Fdx.doi.org%2F10.1093%2Fbiostatistics%2Fkxv043