Copas' method is sensitive to different mechanisms of publication bias

广义线性混合模型下稀有事件荟萃分析中发表偏倚的Copas-Heckman型敏感性分析

Copas-Heckman-type sensitivity analysis for publication bias in rare-event meta-analysis under generalized linear mixed models

针对稀有事件荟萃分析中GLMMs处理发表偏倚（PB）方法有限的问题，提出基于Copas-Heckman型的敏感性分析框架，经模拟和实例验证其可有效评估PB影响且易于实现。

连续型结局元分析中发表偏倚的校正：一项比较研究

Adjusting for publication bias in meta-analysis with continuous outcomes: a comparative study

本研究针对连续型结局元分析的发表偏倚问题，通过案例与模拟研究比较五种校正方法及随机效应元分析的性能，发现PET-PEESE法偏倚小且异方差下稳健，更具应用优势。

基于精确似然的诊断研究荟萃分析中解决发表偏倚的似然敏感性分析

A likelihood-based sensitivity analysis for addressing publication bias in meta-analysis of diagnostic studies using exact likelihood

本文首次将 Copas t 统计量选择模型与双变量二项模型结合，用于诊断研究荟萃分析中的发表偏倚敏感性分析，以解决正态近似在稀疏数据下不适用的问题，并通过真实数据和模拟研究验证了方法的有效性。

Modelling publication bias and p-hacking

Publication bias and p-hacking are two well-known phenomena that strongly affect the scientific literature and cause severe problems in meta-analyses. Due to these phenomena, the assumptions of meta-analyses are seriously violated and the results of the studies cannot be trusted. While publication bias is almost perfectly captured by the weighting function selection model, p-hacking is much harder to model and no definitive solution has been found yet. In this paper we propose to model both publication bias and p-hacking with selection models. We derive some properties for these models, and we…

Simulation Models for Aggregated Data Meta-Analysis: Evaluation of Pooling Effect Sizes and Publication Biases

Simulation studies are commonly used to evaluate the performance of newly developed meta-analysis methods. For methodology that is developed for an aggregated data meta-analysis, researchers often resort to simulation of the aggregated data directly, instead of simulating individual participant data from which the aggregated data would be calculated in reality. Clearly, distributional characteristics of the aggregated data statistics may be derived from distributional assumptions of the underlying individual data, but they are often not made explicit in publications. This paper provides the di…

超越发表偏倚检测：估计异质文献不确定性下的偏倚

Beyond Publication-Bias Detection: Estimating Bias under Uncertainty in Heterogeneous Literatures

研究指出元分析中发表偏倚测试功效低，应转向用置信区间估计偏倚，逐步函数选择模型置信区间过窄，z曲线的置信区间有标称覆盖率，是检测发表偏倚的 valuable 工具，还通过实例说明偏倚估计意义。