Open Access
2018 Categorizing a continuous predictor subject to measurement error
Betsabé G. Blas Achic, Tianying Wang, Ya Su, Victor Kipnis, Kevin Dodd, Raymond J. Carroll
Electron. J. Statist. 12(2): 4032-4056 (2018). DOI: 10.1214/18-EJS1489


Epidemiologists often categorize a continuous risk predictor, even when the true risk model is not a categorical one. Nonetheless, such categorization is thought to be more robust and interpretable, and thus their goal is to fit the categorical model and interpret the categorical parameters. We address the question: with measurement error and categorization, how can we do what epidemiologists want, namely to estimate the parameters of the categorical model that would have been estimated if the true predictor was observed? We develop a general methodology for such an analysis, and illustrate it in linear and logistic regression. Simulation studies are presented and the methodology is applied to a nutrition data set. Discussion of alternative approaches is also included.


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Betsabé G. Blas Achic. Tianying Wang. Ya Su. Victor Kipnis. Kevin Dodd. Raymond J. Carroll. "Categorizing a continuous predictor subject to measurement error." Electron. J. Statist. 12 (2) 4032 - 4056, 2018.


Received: 1 January 2018; Published: 2018
First available in Project Euclid: 11 December 2018

zbMATH: 07003236
MathSciNet: MR3885744
Digital Object Identifier: 10.1214/18-EJS1489

Keywords: Categorization , differential misclassification , epidemiology practice , Inverse problems , measurement error

Vol.12 • No. 2 • 2018
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