  Meta-analyses of diagnostic test accuracy (DTA) studies have been gaining
prominence in research in clinical epidemiology and health technology
development. In these DTA meta-analyses, some studies may have markedly
different characteristics from the others, and potentially be inappropriate to
include. The inclusion of these "outlying" studies might lead to biases,
yielding misleading results. In addition, there might be influential studies
that have notable impacts on the results. In this article, we propose Bayesian
methods for detecting outlying studies and their influence diagnostics in DTA
meta-analyses. Synthetic influence measures based on the bivariate hierarchical
Bayesian random effects models are developed because the overall influences of
individual studies should be simultaneously assessed by the two outcome
variables and their correlation information. We propose four synthetic measures
for influence analyses: (1) relative distance, (2) standardized residual, (3)
Bayesian p-value, and (4) influence statistic on the area under the summary
receiver operating characteristic curve. We also show conventional univariate
Bayesian influential measures can be applied to the bivariate random effects
models, which can be used as marginal influential measures. We illustrate the
effectiveness of the proposed methods by applying them to a DTA meta-analysis
of ultrasound in screening for vesicoureteral reflux among children with
urinary tract infections.
