ERIC Number: EJ1109045
Record Type: Journal
Publication Date: 2014-Dec
Abstractor: As Provided
Methods for the Joint Meta-Analysis of Multiple Tests
Trikalinos, Thomas A.; Hoaglin, David C.; Small, Kevin M.; Terrin, Norma; Schmid, Christopher H.
Research Synthesis Methods, v5 n4 p294-312 Dec 2014
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.
Descriptors: Meta Analysis, Diagnostic Tests, Accuracy, Comparative Analysis, Correlation, Bayesian Statistics, Down Syndrome, Simulation, Regression (Statistics), Maximum Likelihood Statistics, Multiple Choice Tests
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Publication Type: Journal Articles; Reports - Research
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