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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Regression calibration for dichotomized mismeasured predictors.

Loki Natarajan1

  • 1University of California, San Diego, USA.

The International Journal of Biostatistics
|January 5, 2010
PubMed
Summary
This summary is machine-generated.

Dichotomizing mismeasured exposure data can cause bias in epidemiologic studies. This research introduces a regression calibration method to correct for this bias, improving exposure-disease association estimates.

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Last Updated: Jun 17, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Epidemiologic research estimates exposure-disease associations, crucial for public health.
  • Exposure variables with measurement errors can introduce bias into regression models.
  • Dichotomizing continuous exposures, often for public health thresholds, can exacerbate these biases.

Purpose of the Study:

  • To investigate biases arising from dichotomizing a mismeasured continuous exposure.
  • To develop and evaluate a measurement error correction method for this specific scenario.

Main Methods:

  • Focus on biases related to dichotomizing mismeasured continuous exposures.
  • Discusses bias amount in relation to measurement error and cut point choice.
  • Develops regression calibration for bias correction and compares it to naive methods.

Main Results:

  • Simulations assess the properties of the measurement error correction method, including bias and mean-squared error.
  • Quantifies the impact of measurement error and dichotomization cut points on bias.
  • Demonstrates the effectiveness of regression calibration compared to naive approaches.

Conclusions:

  • Measurement error in dichotomized exposures introduces bias in epidemiologic models.
  • Regression calibration offers a viable method for correcting bias in these situations.
  • Accurate estimation of exposure-disease associations requires addressing measurement error in dichotomized variables.