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A composite likelihood method for bivariate meta-analysis in diagnostic systematic reviews.

Yong Chen1, Yulun Liu1, Jing Ning2

  • 11 Division of Biostatistics, The University of Texas Health Science Center at Houston, Houston, USA.

Statistical Methods in Medical Research
|December 17, 2014
PubMed
Summary

A new composite likelihood (CL) method improves bivariate meta-analysis for diagnostic systematic reviews. This approach offers computational simplicity and robustness, overcoming issues with small study numbers in diagnostic test evaluations.

Keywords:
Bivariate generalized linear mixed effects modelcomposite likelihooddiagnostic accuracydiagnostic reviewmeta-analysis

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Area of Science:

  • Medical Statistics
  • Diagnostic Test Evaluation
  • Meta-Analysis

Background:

  • Diagnostic systematic reviews are crucial for evaluating diagnostic technologies.
  • Meta-analysis often involves pooling sensitivity and specificity data from multiple studies.
  • Standard methods can face convergence issues, especially with limited data.

Purpose of the Study:

  • To introduce a composite likelihood (CL) method for bivariate meta-analysis in diagnostic systematic reviews.
  • To provide an alternative for inferring diagnostic measures like sensitivity, specificity, and diagnostic odds ratio.
  • To address limitations of the standard likelihood method in diagnostic meta-analysis.

Main Methods:

  • Proposed a composite likelihood (CL) method for bivariate meta-analysis.
  • Applied the CL method to diagnostic systematic reviews.
  • Conducted simulation studies to compare CL method with standard likelihood methods.

Main Results:

  • The CL method avoids nonconvergence problems common in small sample sizes.
  • Simulation studies demonstrated high relative efficiency for the CL method compared to standard methods.
  • The CL method offers computational simplicity and robustness to model misspecification.

Conclusions:

  • The composite likelihood method is a viable and advantageous alternative for bivariate meta-analysis in diagnostic reviews.
  • This method enhances the reliability of diagnostic measure inference, particularly with limited study data.
  • The CL method was successfully illustrated in a review of melanoma metastasis detection technologies.