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Area of Science:

  • Biostatistics
  • Medical Informatics
  • Diagnostic Accuracy Research

Background:

  • The analysis of diagnostic studies often involves summarizing Receiver Operating Characteristic (SROC) curves.
  • Existing methods for meta-analysis of diagnostic studies are frequently bivariate.
  • The Lehmann model provides a framework for ROC curves using study-specific sensitivities and specificities.

Purpose of the Study:

  • To propose a proportional hazards measure for analyzing SROC curves in diagnostic meta-analysis.
  • To introduce a univariate approach for diagnostic meta-analysis using mixed modeling.
  • To facilitate the application of conventional mixed modeling techniques to diagnostic accuracy data.

Main Methods:

  • Estimating study-specific models and using diagnostic accuracy as an outcome measure in a mixed model.
  • Incorporating random study effects and fixed study-level covariates.
  • Deriving within-study variances for estimable variance component models, enabling a univariate approach.

Main Results:

  • Demonstrated the feasibility of fitting mixed models for meta-analytic diagnostic data using modified SAS procedures (proc mixed).
  • Illustrated the methodology with several meta-analytic diagnostic datasets.
  • Included a meta-analysis of the Mini-Mental State Examination for dementia and mild cognitive impairment detection.

Conclusions:

  • The proposed methodology integrates diagnostic meta-analysis into established mixed modeling frameworks.
  • Discusses various outcome measures for diagnostic accuracy, distinguishing between local and global assessments.
  • Provides recommendations for choosing optimal cut-off values and addresses this challenge within the proposed methodology.