Electronic Journal of Statistics

Categorizing a continuous predictor subject to measurement error

Betsabé G. Blas Achic, Tianying Wang, Ya Su, Victor Kipnis, Kevin Dodd, and Raymond J. Carroll

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Abstract

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.

Article information

Source
Electron. J. Statist., Volume 12, Number 2 (2018), 4032-4056.

Dates
Received: January 2018
First available in Project Euclid: 11 December 2018

Permanent link to this document
https://projecteuclid.org/euclid.ejs/1544518836

Digital Object Identifier
doi:10.1214/18-EJS1489

Mathematical Reviews number (MathSciNet)
MR3885744

Zentralblatt MATH identifier
07003236

Keywords
Categorization differential misclassification epidemiology practice inverse problems measurement error

Rights
Creative Commons Attribution 4.0 International License.

Citation

Blas Achic, Betsabé G.; Wang, Tianying; Su, Ya; Kipnis, Victor; Dodd, Kevin; Carroll, Raymond J. Categorizing a continuous predictor subject to measurement error. Electron. J. Statist. 12 (2018), no. 2, 4032--4056. doi:10.1214/18-EJS1489. https://projecteuclid.org/euclid.ejs/1544518836


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