  In meta-analysis of diagnostic test accuracy, summary receiver operating
characteristic (SROC) is a recommended method to summarize the discriminant
capacity of a diagnostic test in the presence of study-specific cutoff values
and the area under the SROC (SAUC) gives the aggregate measure of test
accuracy. SROC or SAUC can be estimated by bivariate modelling of pairs of
sensitivity and specificity over the primary diagnostic studies. However,
publication bias is a major threat to the validity of estimates in
meta-analysis. To address this issue, we propose to adopt sensitivity analysis
to make an objective inference for the impact of publication bias on SROC or
SAUC. We extend Copas likelihood based sensitivity analysis to the bivariate
normal model used for meta-analysis of diagnostic test accuracy to evaluate how
much SROC or SAUC would change with different selection probabilities under
several selective publication mechanisms dependent on sensitivity and/or
specificity. The selection probability is modelled by a selection function on
$t$-type statistic for the linear combination of logit-transformed sensitivity
and specificity, allowing the selective publication of each study to be
influenced by the cutoff-dependent $p$-value for sensitivity, specificity, or
diagnostic odds ratio. By embedding the selection function into the bivariate
normal model, the conditional likelihood is proposed and the bias-corrected
SROC or SAUC can be estimated by maximizing the likelihood. We illustrate the
proposed sensitivity analysis by reanalyzing a meta-analysis of test accuracy
for intravascular device related infection. Simulation studies are conducted to
investigate the performance of proposed methods.

Methodology;Other Statistics