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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

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Updated: May 20, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

A conditional likelihood approach for regression analysis using biomarkers measured with batch-specific error.

Ming Wang1, W Dana Flanders, Roberd M Bostick

  • 1Department of Biostatistics and Bioinformatics, Emory University Rollins School of Public Health, Atlanta, GA 30322, USA.

Statistics in Medicine
|July 25, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a robust conditional likelihood method to address correlated measurement error in predictors common in biomedical studies. The approach improves regression analysis accuracy, especially in logistic regression, outperforming existing methods.

Related Experiment Videos

Last Updated: May 20, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

  • Epidemiology
  • Biostatistics
  • Biomedical Research

Background:

  • Measurement error is prevalent in epidemiological and biomedical studies.
  • Batch-specific correlated measurement error in predictors poses challenges for standard regression analyses.
  • Existing methods often fail when predictors have batch-specific measurement error.

Purpose of the Study:

  • To develop a robust method for regression analysis that accounts for batch-specific measurement error in predictors.
  • To evaluate the performance of the proposed method against existing approaches through simulations.
  • To address limitations of current methods in generalized linear models, particularly logistic regression.

Main Methods:

  • A robust conditional likelihood approach is proposed to handle additive batch-specific measurement error.
  • The method requires no assumptions on the distribution of measurement error.
  • A hybrid approach combining conditional likelihood and regression calibration is also examined.

Main Results:

  • The conditional likelihood approach demonstrates superior finite sample performance compared to regression calibration and naive methods.
  • For logistic regression, the proposed method outperforms regression with batch as a covariate.
  • The hybrid approach shows good performance for combined batch-specific and measurement-specific errors.

Conclusions:

  • The robust conditional likelihood approach effectively addresses batch-specific measurement error in regression analysis.
  • This method offers improved accuracy, particularly for logistic regression models in biomedical research.
  • The study provides a valuable tool for handling complex error structures in observational data.