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Approximate and Pseudo-Likelihood Analysis for Logistic Regression Using External Validation Data to Model Log

Robert H Lyles1, Lawrence L Kupper

  • 1Department of Biostatistics and Bioinformatics, The Rollins School of Public Health of Emory University, 1518 Clifton Rd. N.E., Mailstop 1518-002-3AA, Atlanta, GA 30322, USA.

Journal of Agricultural, Biological, and Environmental Statistics
|September 13, 2013
PubMed
Summary
This summary is machine-generated.

This study addresses measurement error in environmental epidemiology, particularly for metal working fluid exposure and lung function. A pseudo-likelihood approach is recommended over regression calibration for accurate logistic regression modeling.

Keywords:
ConsistencyLikelihoodMultiplicative measurement errorProbitValidation

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Area of Science:

  • Environmental Epidemiology
  • Biostatistics
  • Occupational Health

Background:

  • Logistic regression is common in environmental epidemiology for disease association studies.
  • Exposure measurement error, especially indirect or multiplicative, complicates accurate modeling.
  • External validation data is crucial for addressing complex exposure measurement issues.

Purpose of the Study:

  • To investigate methods for logistic regression with multiplicative-lognormal measurement error in exposure.
  • To evaluate the performance of pseudo-likelihood (PL) versus regression calibration (RC) and maximum likelihood (ML) methods.
  • To provide strategies for adjusted standard errors in exposure-disease association studies.

Main Methods:

  • Focus on a multiplicative-lognormal structural measurement error model.
  • Comparison of pseudo-likelihood (PL) approach with regression calibration (RC) and probit-based ML.
  • Simulation studies to assess bias and performance under various scenarios.

Main Results:

  • Pseudo-likelihood (PL) demonstrated consistency and fewer computational issues compared to ML.
  • Regression calibration (RC) and ML methods showed potential for considerable bias.
  • PL approach is supported across various exposure measurement error scenarios.

Conclusions:

  • The pseudo-likelihood (PL) method is a robust approach for handling multiplicative-lognormal exposure measurement error in logistic regression.
  • PL offers a more reliable alternative to RC and ML, avoiding restrictive assumptions.
  • Accessible methods for standard error adjustment are provided for both PL and RC estimates.