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20190628 Lifeng Lin:Innovative methods for assessing publication bias
时间:2019-06-16

报告时间:2019年6月28日(星期五)16:30-17:30

报告地点:明德主楼1016会议室

报告题目:Innovative methods for assessing publication bias


报告摘要:Assessing publication bias is a critical procedure in meta-analyses for rating the synthesized overall evidence. On the one hand, examining funnel plots’ asymmetry has been popular to investigate potentially missing studies and bias direction. Most funnel plots present treatment effects against their standard errors, and the contours depicting studies’ significance levels have been used to distinguish publication bias from other confounders (e.g., heterogeneity) that may also cause the plots’ asymmetry. However, treatment effects and their standard errors are frequently associated even if no publication bias exists, so standard-error-based funnel plots may lead to false positive conclusions when such association may not be negligible. In addition, the missingness of studies may relate to their sample sizes besides P values. Therefore, funnel plots based on sample sizes can be an alternative tool. However, the contours for standard-error-based funnel plots cannot be directly applied to sample-size-based ones. We introduce contours for sample-size-based funnel plots of various effect sizes, which may help meta-analysts properly appraise publication bias.

On the other hand, 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.


报告人简介:Dr. Lin is an Assistant Professor in the Department of Statistics at Florida State University. He obtained his BS in Statistics at the University of Science and Technology of China in 2013 and his PhD in Biostatistics at the University of Minnesota in 2017. His research interests focus on computational and theoretical Bayesian methods, and efficient and robust statistical methods for meta-analyses and mixed treatment comparisons. His work has been published on various biostatistical, epidemiological, and medical journals, including Biometrics, Journal of Clinical Epidemiology, and British Medical Journal.