  The area under the curve (AUC) of summary receiver operating characteristic
(SROC) curve is a primary statistical outcome for meta-analysis of diagnostic
test accuracy studies (DTA). However, its confidence interval has not been
reported in most of DTA meta-analyses, because no certain methods and
statistical packages have been provided. In this article, we provide a
bootstrap algorithm for computing the confidence interval of the AUC. Also,
using the bootstrap framework, we can conduct a bootstrap test for assessing
significance of the difference of AUCs for multiple diagnostic tests. In
addition, we provide an influence diagnostic method based on the AUC by
leave-one-study-out analyses. We present illustrative examples using two DTA
met-analyses for diagnostic tests of cervical cancer and asthma. We also
developed an easy-to-handle R package dmetatools for these computations. The
various quantitative evidence provided by these methods certainly supports the
interpretations and precise evaluations of statistical evidence of DTA
meta-analyses.
