A0357
Title: Nonparametric worst-case bounds for publication bias on the summary receiver operating characteristic curve
Authors: Satoshi Hattori - The University of Osaka (Japan) [presenting]
Abstract: 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, the selective publication of diagnostic studies for meta-analysis can induce publication bias (PB) in the estimate of the SROC curve. Several sensitivity analysis methods have been developed to quantify PB on the S ROC curve, and all these methods utilize parametric selection functions to model the selective publication mechanism. A new sensitivity analysis approach is proposed 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 approximate the bias on the SROC curves along with the corresponding area under the curves, and then the maximum and minimum values of PB under a range of marginal selection probabilities are optimized by nonlinear programming. The proposed method is applied to real-world meta-analyses 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.
