  The summary receiver operating characteristic (SROC) curve has been
recommended as one important meta-analytical summary to represent the accuracy
of a diagnostic test in the presence of heterogeneous cutoff values. However,
selective publication of diagnostic studies for meta-analysis can induce
publication bias (PB) on the estimate of the SROC curve. Several sensitivity
analysis methods have been developed to quantify PB on the SROC curve, and all
these methods utilize parametric selection functions to model the selective
publication mechanism. The main contribution of this article is to propose a
new sensitivity analysis approach that derives the worst-case bounds for the
SROC curve by adopting nonparametric selection functions under minimal
assumptions. The estimation procedures of the worst-case bounds use the Monte
Carlo method to obtain the SROC curves along with the corresponding area under
the curves in the worst case where the maximum possible PB under a range of
marginal selection probabilities is considered. We apply the proposed method to
a real-world meta-analysis to show that the worst-case bounds of the SROC
curves can provide useful insights for discussing the robustness of
meta-analytical findings on diagnostic test accuracy.
