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Generalized linear mixed model for binary outcomes when covariates are subject to measurement errors and detection

Xianhong Xie1, Xiaonan Xue1, Howard D Strickler1

  • 1Department of Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, NY, 10461, USA.

Statistics in Medicine
|October 6, 2017
PubMed
Summary

This study introduces a new statistical method, Monte Carlo Newton-Raphson (MCNR), for analyzing biomarker data with measurement error and left-censoring. The MCNR method demonstrates superior performance in risk factor analysis for disease endpoints compared to existing techniques.

Keywords:
Monte Carlo Newton-Raphsondetection limitgeneralized linear mixed modellongitudinal datameasurement error

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Biomarker Analysis

Background:

  • Longitudinal biomarker measurements are crucial for identifying disease risk factors.
  • Biomarkers often exhibit measurement error and left-censoring due to detection limits.
  • Existing statistical methods for these challenges are limited.

Purpose of the Study:

  • To propose a novel statistical method for analyzing longitudinal biomarker data with measurement error and left-censoring.
  • To evaluate the performance of the proposed method against existing approaches.
  • To apply the method to real-world data on HIV viral load and HPV detection.

Main Methods:

  • Development of a generalized linear mixed model.
  • Parameter estimation using the Monte Carlo Newton-Raphson (MCNR) method.
  • Inference via Louis's method and the delta method.
  • Comparison with Maximum Likelihood (ML) and Half of Detection Limit (HDL) imputation methods via simulations.

Main Results:

  • The MCNR method outperformed ML and HDL methods in simulation studies.
  • MCNR showed better empirical standard error and 95% confidence interval coverage probability.
  • MCNR avoids computational constraints related to quadrature points faced by ML and HDL.

Conclusions:

  • The proposed MCNR method offers a superior approach for analyzing longitudinal biomarker data with measurement error and left-censoring.
  • MCNR provides more accurate and reliable inferences compared to traditional methods.
  • The method's utility is confirmed in a study of HIV viral load and HPV detection in women.