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.

翻译：综合受试者工作特征（SROC）曲线被推荐作为在存在异质性截断值时表示诊断试验准确性的重要元分析汇总指标。然而，诊断研究的选择性发表会导致元分析对SROC曲线估计产生发表偏倚（PB）。目前已开发出多种灵敏度分析方法用于量化SROC曲线上的PB，这些方法均采用参数化选择函数来建模选择性发表机制。本文的主要贡献在于提出一种新的灵敏度分析方法，该方法通过采用最小假设下的非参数选择函数，推导出SROC曲线的最坏情况界限。最坏情况界限的估计过程采用蒙特卡洛方法，在考虑边际选择概率范围内最大可能PB的最坏情况下，获取SROC曲线及其对应的曲线下面积。我们将所提方法应用于一项真实世界元分析，结果表明SROC曲线的最坏情况界限可为讨论诊断试验准确性元分析结果的稳健性提供有价值的见解。