Permutation inference methods for multivariate meta‐analysis
Permutation inference methods for multivariate meta‐analysis
复制标题
多元荟萃分析的排列推理方法
DOI：
10.1111/biom.13134
复制
发表时间：
2019
期刊：
影响因子：
1.9
通讯作者：
Furukawa Toshi A.
中科院分区：
文献类型：
--
作者：
Noma Hisashi;Nagashima Kengo;Furukawa Toshi A.
多变量荟萃分析在证据合成研究中越来越突出，因为它可以同时合成多个相关的结果数据，随机效应模型通常用于解决研究间的异质性。然而，随机效应模型的标准推断方法（例如，限制性最大似然估计）的置信区域或区间的覆盖概率通常不能保持其标称置信水平，特别是当合成研究的数量较少时，因为其有效性取决于大样本近似。在这篇文章中，我们提供了基于置换的推理方法，使精确的联合推理的平均结果的措施，没有大样本近似。我们还提供了准确的边际推理方法下的一般设置的多元荟萃分析。我们提出了有效的方法，置换推理使用最佳加权的基础上有效的得分统计。所提出的方法的有效性说明通过应用到双变量荟萃分析的诊断准确性研究气道嗜酸性粒细胞增多症哮喘和抗高血压药物的网络荟萃分析事件糖尿病，以及通过模拟实验。在通过模拟进行的数值评估中，我们的方法通常在广泛的设置范围内提供准确的置信区域或区间，而目前的标准推理方法表现出严重的覆盖不足特性。
Multivariate meta-analysis is gaining prominence in evidence synthesis research because it enables simultaneous synthesis of multiple correlated outcome data, and random-effects models have generally been used for addressing between-studies heterogeneities. However, coverage probabilities of confidence regions or intervals for standard inference methods for random-effects models (eg, restricted maximum likelihood estimation) cannot retain their nominal confidence levels in general, especially when the number of synthesized studies is small because their validities depend on large sample approximations. In this article, we provide permutation-based inference methods that enable exact joint inferences for average outcome measures without large sample approximations. We also provide accurate marginal inference methods under general settings of multivariate meta-analyses. We propose effective approaches for permutation inferences using optimal weighting based on the efficient score statistic. The effectiveness of the proposed methods is illustrated via applications to bivariate meta-analyses of diagnostic accuracy studies for airway eosinophilia in asthma and a network meta-analysis for antihypertensive drugs on incident diabetes, as well as through simulation experiments. In numerical evaluations performed via simulations, our methods generally provided accurate confidence regions or intervals under a broad range of settings, whereas the current standard inference methods exhibited serious undercoverage properties.