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Title: Hybrid test for publication bias in meta-analysis Authors:  Lifeng Lin - Florida State University (United States) [presenting]
Abstract: Assessing publication bias is a critical procedure in meta-analyses for rating the synthesized overall evidence. Many statistical tests have been proposed to detect publication bias. However, they often make dramatically different assumptions about the cause of publication bias; therefore, they are usually powerful only in certain cases that support their particular assumptions, while their powers may be fairly low in many other cases. Although several simulation studies have been conducted to compare different tests' powers under various situations, it is infeasible to justify the exact mechanism of publication bias in a real-world meta-analysis and thus select the optimal publication bias test. We propose a hybrid test for publication bias by synthesizing various tests and incorporating their benefits, so that it maintains relatively high powers across various mechanisms of publication bias. The superior performance of the proposed hybrid test is illustrated using simulation studies and three real-world meta-analyses with different effect sizes. It is compared with many existing methods.