Contribution to the discussion of "When should meta-analysis avoid making hidden normality assumptions?": A Bayesian perspective

A Robust Bayesian Copas Selection Model for Quantifying and Correcting Publication Bias

The validity of conclusions from meta-analysis is potentially threatened by publication bias. Most existing procedures for correcting publication bias assume normality of the study-specific effects that account for between-study heterogeneity. However, this assumption may not be valid, and the performance of these bias correction procedures can be highly sensitive to departures from normality. Further, there exist few measures to quantify the magnitude of publication bias based on selection models. In this paper, we address both of these issues. First, we explore the use of heavy-tailed distri…

Testing normality using the summary statistics with application to meta-analysis

As the most important tool to provide high-level evidence-based medicine, researchers can statistically summarize and combine data from multiple studies by conducting meta-analysis. In meta-analysis, mean differences are frequently used effect size measurements to deal with continuous data, such as the Cohen's d statistic and Hedges' g statistic values. To calculate the mean difference based effect sizes, the sample mean and standard deviation are two essential summary measures. However, many of the clinical reports tend not to directly record the sample mean and standard deviation. Instead, t…

无需正态性：基于惩罚高斯混合的真效应分布估计

Meta-Analysis Without Normality: Estimating the True Effect Distribution with Penalized Gaussian Mixtures

本文提出基于惩罚高斯混合的方法，用于在不假设正态分布的情况下估计真效应分布，适用于大规模异质数据集，提高尾部概率和密度估计的准确性。

放宽随机效应正态性假设的元分析模型：方法学系统综述与模拟研究

Meta-analysis models relaxing the random effects normality assumption: methodological systematic review and simulation study

本研究系统综述了24种放宽正态性假设的随机效应元分析模型，并通过模拟比较15种模型，发现异质性大时各模型对平均效应估计均有偏，提示需谨慎解读。

利用重要性采样估计全子集元分析中的p值，应用于单细胞eQTL映射

Using Importance Sampling to Estimate $p$-values in All-Subset Meta-Analysis, with Applications to Single-Cell eQTL Mapping

本文提出一种高效的重要性采样算法，用于估计全子集元分析中的p值，解决了传统方法在极端尾部的精度问题，并展示了其在单细胞eQTL映射中的应用。

基于广义线性混合模型的随机效应Meta分析：适用于少量研究的Bartlett校正方法

Random-effects meta-analysis via generalized linear mixed models: A Bartlett-corrected approach for few studies

针对少量研究下广义线性混合模型随机效应Meta分析的研究间方差低估问题，提出带简化Bartlett校正的轮廓似然法，兼具理论保障与良好的模拟和实证表现。