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Maximum likelihood estimation based on Newton-Raphson iteration for the bivariate random effects model in test

Brian H Willis1, Mohammed Baragilly1,2, Dyuti Coomar1

  • 1Institute of Applied Health Research, University of Birmingham, Birmingham, UK.

Statistical Methods in Medical Research
|June 12, 2019
PubMed
Summary

This study presents two new numerical methods for estimating parameters in bivariate generalized linear mixed models used for meta-analysis of test accuracy. These methods offer higher convergence rates and better coverage probabilities than existing software.

Keywords:
Bivariate modeldiagnostic accuracymaximum likelihood estimationmeta-analysisrandom effects

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Area of Science:

  • Biostatistics
  • Medical Informatics
  • Statistical Modeling

Background:

  • Bivariate generalized linear mixed models are crucial for meta-analysis of test accuracy studies.
  • These models are complex, requiring estimation of five parameters.
  • Numerical methods are needed due to the absence of a closed-form likelihood function.

Purpose of the Study:

  • To demonstrate, from first principles, how to obtain maximum likelihood estimates for model parameters using Newton-Raphson iteration.
  • To introduce two distinct methods: one using profile likelihood and another using Observed Fisher Information.
  • To develop robust initial estimation techniques to ensure algorithm convergence.

Main Methods:

  • Developed two Newton-Raphson iteration-based algorithms for parameter estimation.
  • Incorporated methods for obtaining robust initial estimates to improve convergence.
  • Evaluated algorithms via simulation and comparison with the R package 'lme4' (glmer).

Main Results:

  • The proposed algorithms demonstrated higher convergence rates compared to 'glmer'.
  • Coverage probabilities were generally better with the new methods.
  • The profile likelihood method showed favorable performance characteristics.

Conclusions:

  • The two novel algorithms provide effective alternatives for fitting bivariate generalized linear mixed models in meta-analysis.
  • The profile likelihood method is recommended for its performance.
  • These methods enhance the accessibility and reliability of meta-analysis for test accuracy studies.