School of Statistics and Mathematics, and School of Business, Zhejiang Gongshang University, Hangzhou, Zhejiang, China.
School of Mathematical Sciences, Nankai University, Tianjin, China.
BMC Med Res Methodol. 2020 Apr 30;20(1):98. doi: 10.1186/s12874-020-00954-8.
Meta-analysis provides a useful statistical tool to effectively estimate treatment effect from multiple studies. When the outcome is binary and it is rare (e.g., safety data in clinical trials), the traditionally used methods may have unsatisfactory performance.
We propose using importance sampling to compute confidence intervals for risk difference in meta-analysis with rare events. The proposed intervals are not exact, but they often have the coverage probabilities close to the nominal level. We compare the proposed accurate intervals with the existing intervals from the fixed- or random-effects models and the interval by Tian et al. (2009).
We conduct extensive simulation studies to compare them with regards to coverage probability and average length, when data are simulated under the homogeneity or heterogeneity assumption of study effects.
The proposed accurate interval based on the random-effects model for sample space ordering generally has satisfactory performance under the heterogeneity assumption, while the traditionally used interval based on the fixed-effects model works well when the studies are homogeneous.
元分析提供了一种有用的统计工具，可以有效地从多项研究中估计治疗效果。当结局是二分类且罕见（例如临床试验中的安全性数据）时，传统方法的性能可能不尽如人意。
我们提出使用重要抽样来计算元分析中风险差异的置信区间，用于罕见事件。所提出的区间不是精确的，但它们通常具有接近名义水平的覆盖概率。我们将提出的准确区间与固定效应或随机效应模型以及 Tian 等人（2009 年）的区间进行比较。
我们进行了广泛的模拟研究，以比较在研究效果同质性或异质性假设下，数据模拟时这些区间在覆盖概率和平均长度方面的表现。
基于样本空间排序的随机效应模型的拟准确区间在异质性假设下通常表现良好，而传统的基于固定效应模型的区间在研究同质时效果良好。
