ANOVA model for network meta-analysis of diagnostic test accuracy data

TL;DR

This paper introduces a novel hierarchical arm-based model for network meta-analysis of diagnostic test accuracy, effectively handling the correlation between sensitivity and specificity and providing more interpretable results.

Contribution

The paper proposes a simple, hierarchical arm-based model for NMA of diagnostic accuracy studies, improving interpretability and data utilization over existing contrast-based models.

Findings

The AB model performs well on real meta-analyses of cervical cancer tests.

It yields easily interpretable marginal means.

It accommodates complex variance-covariance structures.

Abstract

Network meta-analysis (NMA) allow combining efficacy information from multiple comparisons from trials assessing different therapeutic interventions for a given disease and to estimate unobserved comparisons from a network of observed comparisons. Applying NMA on diagnostic accuracy studies is a statistical challenge given the inherent correlation of sensitivity and specificity. A conceptually simple and novel hierarchical arm-based (AB) model which expresses the logit transformed sensitivity and specificity as sum of fixed effects for test, correlated study-effects and a random error associated with various tests evaluated in given study is proposed. We apply the model to previously published meta-analyses assessing the accuracy of diverse cytological and molecular tests used to triage women with minor cervical lesions to detect cervical precancer and the results compared with those from the contrast-based (CB) model which expresses the linear predictor as a contrast to a comparator test. The proposed AB model is more appealing than the CB model in that it yields the marginal means which are easily interpreted and makes use of all available data and easily accommodates more general variance-covariance matrix structures.