Research Synthesis Methods

Methods for the joint meta‐analysis of multiple tests

Journal Article

  • Author(s): Thomas A. Trikalinos, David C. Hoaglin, Kevin M. Small, Norma Terrin, Christopher H. Schmid
  • Article first published online: 07 May 2014
  • DOI: 10.1002/jrsm.1115
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Existing methods for meta‐analysis of diagnostic test accuracy focus primarily on a single index test. We propose models for the joint meta‐analysis of studies comparing multiple index tests on the same participants in paired designs. These models respect the grouping of data by studies, account for the within‐study correlation between the tests' true‐positive rates (TPRs) and between their false‐positive rates (FPRs) (induced because tests are applied to the same participants), and allow for between‐study correlations between TPRs and FPRs (such as those induced by threshold effects). We estimate models in the Bayesian setting. We demonstrate using a meta‐analysis of screening for Down syndrome with two tests: shortened humerus (arm bone), and shortened femur (thigh bone). Separate and joint meta‐analyses yielded similar TPR and FPR estimates. For example, the summary TPR for a shortened humerus was 35.3% (95% credible interval (CrI): 26.9, 41.8%) versus 37.9% (27.7, 50.3%) with joint versus separate meta‐analysis. Joint meta‐analysis is more efficient when calculating comparative accuracy: the difference in the summary TPRs was 0.0% (−8.9, 9.5%; TPR higher for shortened humerus) with joint versus 2.6% (−14.7, 19.8%) with separate meta‐analyses. Simulation and empirical analyses are needed to refine the role of the proposed methodology. Copyright © 2014 John Wiley & Sons, Ltd.

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