Measurement error models with interactions

Publication Type:
Journal Article
Citation:
Biostatistics, 2016, 17 (2), pp. 277 - 290
Issue Date:
2016-04-01
Full metadata record
Files in This Item:
Filename Description Size
kxv043.pdfPublished Version147.54 kB
Adobe PDF
© 2015 Published by Oxford University Press 2015. 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 ($W$) 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.
Please use this identifier to cite or link to this item: