An integrative shrinkage estimator for random-effects meta-analysis of rare binary events.
An integrative shrinkage estimator for random-effects meta-analysis of rare binary events.
复制标题
用于罕见二元事件随机效应荟萃分析的综合收缩估计器。
DOI：
10.1016/j.conctc.2018.04.004
复制
发表时间：
2018
影响因子：
1.5
通讯作者：
Wang,Xinlei
中科院分区：
文献类型：
--
作者：
Li,Lie;Bai,Ou;Wang,Xinlei
荟萃分析是一种强大的工具，可以从多个罕见二元事件的研究中推断两种实验条件之间的治疗效果。最近，在随机效应（RE）模型下，Bhaumik等人开发了一种简单平均（SA）估计量，并表明在连续性校正因子为0.5时，SA估计量在一组常用估计量中偏差最小。在本文中，在允许具有相等和不相等可变性（任何方向）的处理组的各种RE模型下，我们基于SA估计器开发了一个综合收缩（iSHRI）估计器，其目的是在考虑偏差-方差权衡的均方误差（MSE）方面提高估计效率。通过仿真，我们发现iSHRI在偏置、MSE、I型误差和置信区间覆盖方面总体上比现有方法有更好的性能。还提供了罗格列酮荟萃分析的数据示例，其中iSHRI产生了具有竞争力的结果。
Meta-analysis has been a powerful tool for inferring the treatment effect between two experimental conditions from multiple studies of rare binary events. Recently, under a random-effects (RE) model, Bhaumik et al. developed a simple average (SA) estimator and showed that with the continuity correction factor 0.5, the SA estimator was the least biased among a set of commonly used estimators. In this paper, under various RE models that allow for treatment groups with equal and unequal variability (in either direction), we develop an integrative shrinkage (iSHRI) estimator based on the SA estimator, which aims to improve estimation efficiency in terms of mean squared error (MSE) that accounts for the bias-variance tradeoff. Through simulation, we find that iSHRI has better performance in general when compared with existing methods, in terms of bias, MSE, type I error and confidence interval coverage. Data examples of rosiglitazone meta-analysis are provided as well, where iSHRI yields competitive results.