Methods for random-effects meta-analysis require an estimate of the between-study variance, $τ^2$. The performance of estimators of $τ^2$ (measured by bias and coverage) affects their usefulness in assessing heterogeneity of study-level effects, and also the performance of related estimators of the overall effect. For the effect measure log-response-ratio (LRR, also known as the logarithm of the ratio of means, RoM), we review four point estimators of $τ^2$ (the popular methods of DerSimonian-Laird (DL), restricted maximum likelihood, and Mandel and Paule (MP), and the less-familiar method of Jackson), four interval estimators for $τ^2$ (profile likelihood, Q-profile, Biggerstaff and Jackson, and Jackson), five point estimators of the overall effect (the four related to the point estimators of $τ^2$ and an estimator whose weights use only study-level sample sizes), and seven interval estimators for the overall effect (four based on the point estimators for $τ^2$, the Hartung-Knapp-Sidik-Jonkman (HKSJ) interval, a modification of HKSJ that uses the MP estimator of $τ^2$ instead of the DL estimator, and an interval based on the sample-size-weighted estimator). We obtain empirical evidence from extensive simulations of data from lognormal distributions.
From Radar to Risk: Building a High-Resolution Hail Database for Austria And Estimating Risk Through the Integration of Distributional Neural Networks into the Metastatistical Framework
Estimating Interventional Distributions with Uncertain Causal Graphs through Meta-Learning
Imperial College London（伦敦帝国学院）；University of Cambridge（剑桥大学）；University of Toronto（多伦多大学）；Vector Institute（向量研究所）；Alan Turing Institute（艾伦·图灵研究所）；University of Oxford（牛津大学）
Estimating Gaussian graphical models of multi-study data with Multi-Study Factor Analysis
University of Massachusetts - Amherst（马萨诸塞大学阿默斯特分校）；Harvard School of Public Health（哈佛公共卫生学院）；Brigham and Women’s Hospital（布莱根妇女医院）；Northwestern University Feinberg School of Medicine（西北大学范伯格医学院）；Sapienza University of Rome（罗马智慧大学）；Brown University（布朗大学）
Simulation-based Estimation of Mean and Standard Deviation for Meta-analysis via Approximate Bayesian Computation (ABC)
背景资料：当对连续结局进行荟萃分析时，需要估计所选研究的平均值和标准差，以获得平均效应及其置信区间的总体估计值。如果出版物中没有直接报告这些数量，则必须根据其他报告的汇总统计量（如中位数、最小值、最大值和四分位数）估计这些数量。研究方法：我们提出了一种基于模拟的估计方法，使用近似贝叶斯计算（ABC）技术估计平均值和标准差的基础上，在已发表的研究中发现的各种汇总统计。我们进行了一项模拟研究，将提出的ABC方法与Hozo et al.（2005），Bland（2015）和Wan et al.（2014）的现有方法进行比较。结果：在标准差的估计中，我们的ABC方法在偏态或重尾分布中表现最好。随着样本量的增加，平均相对误差（ARE）趋于零。在正态分布中，我们的ABC表现良好。然而，Wan等人的方法是最好的，因为它基于正态分布假设。当分布是偏态或重尾分布时，Wan等人的ARE会随着样本量的增加而远离零。在均值的估计中，我们的ABC方法是最好的，因为ARE收敛到零。结论：ABC是一种灵活的方法，用于估计荟萃分析的研究特定平均值和标准差，特别是在潜在的偏态或重尾分布的情况下。当采用贝叶斯分析时，ABC方法可以使用其他报告的汇总统计量，例如后验均值和95%可信区间。
