Background Meta-analysis has become increasingly popular in recent years, especially in genomic data analysis, due to the fast growth of available data and studies that target the same questions. Many methods have been developed, including classical ones such as Fisher’s combined probability test and Stouffer’s Z-test. However, not all meta-analyses have the same goal in mind. Some aim at combining information to find signals in at least one of the studies, while others hope to find more consistent signals across the studies. While many classical meta-analysis methods are developed with the former goal in mind, the latter goal has much more practicality for genomic data analysis. Results In this paper, we propose a class of meta-analysis methods based on summaries of weighted ordered p-values (WOP) that aim at detecting significance in a majority of studies. We consider weighted versions of classical procedures such as Fisher’s method and Stouffer’s method where the weight for each p-value is based on its order among the studies. In particular, we consider weights based on the binomial distribution, where the median of the p-values are weighted highest and the outlying p-values are down-weighted. We investigate the properties of our methods and demonstrate their strengths through simulations studies, comparing to existing procedures. In addition, we illustrate application of the proposed methodology by several meta-analysis of gene expression data. Conclusions Our proposed weighted ordered p-value (WOP) methods displayed better performance compared to existing methods for testing the hypothesis that there is signal in the majority of studies. They also appeared to be much more robust in applications compared to the rth ordered p-value (rOP) method (Song and Tseng, Ann. Appl. Stat. 2014, 8(2):777–800). With the flexibility of incorporating different p-value combination methods and different weighting schemes, the weighted ordered p-values (WOP) methods have great potential in detecting consistent signal in meta-analysis with heterogeneity.
展开▼
机译：背景近年来，由于可用数据和针对相同问题的研究快速增长，因此荟萃分析已变得越来越流行，尤其是在基因组数据分析中。已经开发了许多方法，包括经典方法，例如Fisher组合概率检验和Stouffer的Z检验。但是，并非所有荟萃分析都具有相同的目标。一些旨在至少在一项研究中组合信息以找到信号，而另一些则希望在整个研究中找到更一致的信号。尽管开发许多经典的荟萃分析方法时都考虑到了前一个目标，但后一个目标对于基因组数据分析具有更大的实用性。结果在本文中，我们提出了一种基于加权有序p值（WOP）汇总的荟萃分析方法，旨在检测大多数研究的重要性。我们考虑经典程序的加权形式，例如Fisher方法和Stouffer方法，其中每个p值的权重基于研究中其顺序。特别是，我们考虑基于二项式分布的权重，其中p值的中位数加权最高，而偏远的p值权重较低。我们调查了我们的方法的属性，并通过与现有程序相比的模拟研究证明了它们的优势。此外，我们通过对基因表达数据的一些荟萃分析说明了所提出方法的应用。结论我们提出的加权有序p值（WOP）方法显示出比现有方法更好的性能，这些方法用于检验大多数研究中存在信号的假设。与第r个有序p值（rOP）方法相比，它们在应用程序中似乎也更健壮（Song and Tseng，Ann。Appl。Stat。2014，8（2）：777-800）。结合不同p值组合方法和不同加权方案的灵活性，加权有序p值（WOP）方法在异质性元分析中检测一致信号方面具有巨大潜力。
展开▼
