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Measurement error models with interactions.

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.

SUBMITTER: Midthune D

PROVIDER: S-EPMC4834948 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature

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Measurement error models with interactions.

Midthune Douglas D Carroll Raymond J RJ Freedman Laurence S LS Kipnis Victor V

Biostatistics (Oxford, England) 20151103 2

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 ...[more]

PMID: 26530858

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