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Categorizing a continuous predictor subject to measurement error.

Betsabé G Blas Achic1, Tianying Wang2, Ya Su3

  • 1Departamento de Estatística, Universidade Federal de Pernambuco, Av. Prof. Moraes Rego, 1235 - Cidade Universitária, Recife-PE-Brasil, CEP: 50670-901.

Electronic Journal of Statistics
|June 25, 2019
PubMed
Summary
This summary is machine-generated.

This study develops a method to accurately estimate categorical risk model parameters despite measurement error and data categorization. It ensures epidemiologists can reliably interpret results as if the true predictor were observed.

Keywords:
Categorizationdifferential misclassificationepidemiology practiceinverse problemsmeasurement error

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Statistical Modeling

Background:

  • Epidemiologists frequently categorize continuous risk predictors for enhanced interpretability, even when the underlying risk model is continuous.
  • This practice, while common, can introduce bias when measurement error is present.
  • The goal is to fit a categorical model and interpret its parameters accurately, mirroring analysis of the true, uncategorized predictor.

Purpose of the Study:

  • To develop a general methodology for estimating categorical risk model parameters when the continuous predictor is subject to measurement error and categorization.
  • To address the challenge of obtaining interpretable categorical parameters that reflect the true, unobserved predictor.
  • To provide a robust approach for epidemiological studies involving categorized continuous predictors.

Main Methods:

  • A general statistical methodology is developed to correct for measurement error and categorization bias.
  • The approach is illustrated within the frameworks of linear and logistic regression models.
  • Simulation studies are employed to validate the proposed methodology.

Main Results:

  • The developed methodology allows for the estimation of categorical model parameters as if the true predictor were observed, accounting for measurement error.
  • Application to a nutrition dataset demonstrates the practical utility of the approach.
  • Simulation results confirm the effectiveness of the method in various scenarios.

Conclusions:

  • The proposed methodology offers a robust solution for epidemiologists seeking to interpret categorical risk models accurately, even with imperfect predictor data.
  • This approach enhances the reliability of parameter estimation and interpretation in the presence of measurement error and categorization.
  • The study provides a valuable tool for epidemiological research, improving the validity of findings derived from categorized continuous predictors.