ANOVA model for network meta-analysis of diagnostic test accuracy data. (June 2018)

• Record Type: Journal Article
• Authors: Nyaga, Victoria N; Aerts, Marc; Arbyn, Marc
• Is Part Of: Statistical methods in medical research. Volume 27:Number 6(2018)
• Page Start: 1766
• Page End: 1784
• Publication Date: 2018-06
• Subjects: Meta-analysis -- network meta-analysis -- diagnostic tests -- hierarchical model -- arm-based; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280216669182
• Languages: English
• ISSNs: 0962-2802

Abstract

Procedures combining and summarising direct and indirect evidence from independent studies assessing the diagnostic accuracy of different tests for the same disease are referred to network meta-analysis. Network meta-analysis provides a unified inference framework and uses the data more efficiently. Nonetheless, handling the inherent correlation between sensitivity and specificity continues to be a statistical challenge. We developed an arm-based hierarchical model which expresses the logit transformed sensitivity and specificity as the sum of fixed effects for test, correlated study-effects to model the inherent correlation between sensitivity and specificity and a random error associated with various tests evaluated in a given study. We present the accuracy of 11 tests used to triage women with minor cervical lesions to detect cervical precancer. Finally, we compare the results with those from a contrast-based model which expresses the linear predictor as a contrast to a comparator test. The proposed arm-based model is more appealing than the contrast-based model since the former permits more straightforward interpretation of the parameters, makes use of all available data yielding shorter credible intervals, and models more natural variance–covariance matrix structures.