School of Computing Sciences, University of East Anglia, Norwich, UK.
Population and Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, Massachusetts, USA.
Res Synth Methods. 2020 May;11(3):426-442. doi: 10.1002/jrsm.1404. Epub 2020 Apr 13.
In random-effects meta-analysis the between-study variance ( τ ) has a key role in assessing heterogeneity of study-level estimates and combining them to estimate an overall effect. For odds ratios the most common methods suffer from bias in estimating τ and the overall effect and produce confidence intervals with below-nominal coverage. An improved approximation to the moments of Cochran's Q statistic, suggested by Kulinskaya and Dollinger (KD), yields new point and interval estimators of τ and of the overall log-odds-ratio. Another, simpler approach (SSW) uses weights based only on study-level sample sizes to estimate the overall effect. In extensive simulations we compare our proposed estimators with established point and interval estimators for τ and point and interval estimators for the overall log-odds-ratio (including the Hartung-Knapp-Sidik-Jonkman interval). Additional simulations included three estimators based on generalized linear mixed models and the Mantel-Haenszel fixed-effect estimator. Results of our simulations show that no single point estimator of τ can be recommended exclusively, but Mandel-Paule and KD provide better choices for small and large numbers of studies, respectively. The KD estimator provides reliable coverage of τ . Inverse-variance-weighted estimators of the overall effect are substantially biased, as are the Mantel-Haenszel odds ratio and the estimators from the generalized linear mixed models. The SSW estimator of the overall effect and a related confidence interval provide reliable point and interval estimation of the overall log-odds-ratio.
在随机效应荟萃分析中，研究间方差（τ）在评估研究水平估计值的异质性和组合这些估计值以估计总体效应方面起着关键作用。对于优势比，最常用的方法在估计τ和总体效应时存在偏差，并产生名义覆盖范围以下的置信区间。由 Kulinskaya 和 Dollinger（KD）提出的 Cochran's Q 统计量矩的改进逼近方法，产生了τ和总体对数优势比的新的点估计和区间估计。另一种更简单的方法（SSW）仅使用基于研究水平样本量的权重来估计总体效应。在广泛的模拟中，我们将我们提出的τ的点和区间估计器与τ的既定点和区间估计器以及总体对数优势比的点和区间估计器（包括 Hartung-Knapp-Sidik-Jonkman 区间）进行了比较。额外的模拟包括基于广义线性混合模型和 Mantel-Haenszel 固定效应估计器的三个估计器。我们的模拟结果表明，不能单独推荐τ的单个点估计器，但 Mandel-Paule 和 KD 分别为小数量和大量研究提供了更好的选择。KD 估计器提供了τ的可靠覆盖范围。基于逆方差加权的总体效应估计值存在很大偏差，Mantel-Haenszel 优势比和广义线性混合模型的估计值也是如此。SSW 总体效应估计器和相关置信区间为总体对数优势比提供了可靠的点估计和区间估计。
