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Related Experiment Video

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A population-averaged approach to diagnostic test meta-analysis.

John S Preisser1, Gul Inan2, James M Powers3

  • 1Department of Biostatistics, University of North Carolina, 3105-F McGavran-Greenberg Hall, CB #7420, Chapel Hill, NC, USA.

Biometrical Journal. Biometrische Zeitschrift
|October 30, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a population-averaged model for diagnostic accuracy meta-analysis, offering population-level insights beyond study-specific results. The new method, using generalized estimating equations, performs comparably to existing models for estimating mean sensitivity and specificity.

Keywords:
diagnostic accuracymarginalizationmeta-analysispopulation-averaged model

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Area of Science:

  • Biostatistics
  • Medical Informatics
  • Epidemiology

Background:

  • Diagnostic accuracy meta-analysis is crucial for disease screening programs.
  • Bivariate generalized linear-mixed models (BGLMM) are common but offer study-specific interpretations.
  • There is a need for methods providing population-level inference on diagnostic test accuracy.

Purpose of the Study:

  • To present a population-averaged (PA) model using generalized estimating equations (GEE) for diagnostic accuracy meta-analysis.
  • To derive marginalized regression parameters from BGLMM for population-level inference.
  • To compare the performance of PA and marginalized BGLMM approaches.

Main Methods:

  • Development of a population-averaged model using generalized estimating equations (GEE).
  • Derivation of marginalized regression parameters from the bivariate generalized linear-mixed model (BGLMM).
  • Monte Carlo simulation studies to compare estimator performance and analysis of two real-world datasets.

Main Results:

  • Both the marginalized BGLMM and GEE with sandwich standard errors demonstrated robust performance.
  • Nominal 95% confidence interval coverage for mean sensitivity and specificity was maintained with 25 or more studies.
  • The methods showed resilience even when the covariance structure of test counts was misspecified.

Conclusions:

  • The proposed population-averaged model offers a valuable alternative for diagnostic accuracy meta-analysis.
  • Both PA-GEE and marginalized BGLMM provide reliable estimates of population-level diagnostic accuracy.
  • These methods are suitable for meta-analyses with a moderate number of studies, even with certain data complexities.