标题： 元分析中估计研究间方差和总体效应的模拟研究 显示英文标题

标题： Simulation study of estimating between-study variance and overall effect in meta-analyses of mean difference

摘要： 随机效应元分析的方法需要一个对研究间方差的估计，$\tau^2$。 估计量$\tau^2$的性能（通过偏差和覆盖度来衡量）会影响它们在评估研究层面效应异质性中的有用性，同时也会影响相关总体效应估计量的性能。 对于效应量均值差（MD），我们回顾了五个点估计量$\tau^2$（流行的方法包括 DerSimonian-Laird、限制最大似然、Mandel 和 Paule（MP）；不太熟悉的 Jackson 方法；以及基于\cite{kulinskaya2004welch}对$Q$统计量分布的改进近似的新的 WT 方法），五个区间估计量$\tau^2$（轮廓似然、Q-轮廓、Biggerstaff 和 Jackson、Jackson 以及新的 WT 方法），六个总体效应的点估计量（五个与$\tau^2$的点估计量相关，以及一个仅使用研究级别样本量作为权重的估计量），以及八个总体效应的区间估计量（五个基于$\tau^2$的点估计量，Hartung-Knapp-Sidik-Jonkman（HKSJ）区间，HKSJ 的修改版本，以及基于样本量加权估计量的区间）。我们通过广泛的模拟和一个例子获得了实证证据。 显示英文摘要

摘要： Methods for random-effects meta-analysis require an estimate of the between-study variance, $\tau^2$. The performance of estimators of $\tau^2$ (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 mean difference (MD), we review five point estimators of $\tau^2$ (the popular methods of DerSimonian-Laird, restricted maximum likelihood, and Mandel and Paule (MP); the less-familiar method of Jackson; and a new method (WT) based on the improved approximation to the distribution of the $Q$ statistic by \cite{kulinskaya2004welch}), five interval estimators for $\tau^2$ (profile likelihood, Q-profile, Biggerstaff and Jackson, Jackson, and the new WT method), six point estimators of the overall effect (the five related to the point estimators of $\tau^2$ and an estimator whose weights use only study-level sample sizes), and eight interval estimators for the overall effect (five based on the point estimators for $\tau^2$, the Hartung-Knapp-Sidik-Jonkman (HKSJ) interval, a modification of HKSJ, and an interval based on the sample-size-weighted estimator). We obtain empirical evidence from extensive simulations and an example.