A statistical methodology for analyzing co-occurrence data from a large sample
A statistical methodology for analyzing co-occurrence data from a large sample
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DOI：
10.1016/j.jbi.2006.11.003
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发表时间：
2007-06-01
影响因子：
4.5
通讯作者：
Markatou, Marianthi
中科院分区：
文献类型：
--
作者：
Cao, Hui;Hripcsak, George;Markatou, Marianthi
在大型数据库中确定条目之间的重要关联是具有挑战性的，因为存在多个同时的假设，并且有能力选择在统计上有意义但在临床上没有意义的弱关联。简单地在所有可能的项目对中应用2测试会导致大多数不适当的关联超过传统的(α=0.05，X(2)=3.94)阈值。人们可以选择一个更严格的门槛来找到更强的关联性，但选择可能是武断的。我们将Diaconis和Efron的体积检验与2p值图相结合，选择了一个更严格、更少随意性的阈值。体积检验调整Z(2)统计量的p值。如果没有真正的关联，调整后的p值(1-p与N-p)的曲线图(其中N-p是p值大于p的测试统计的数量)应该是线性的。曲线图偏离直线的点可以用作阈值。我们使用线性回归以可重复性的方式选择阈值。在一次实验中，我们发现该方法选择了一个与之前通过手动审查关联获得的阈值类似的阈值。(C)2006 Elsevier Inc.保留所有权利。
Determining important associations among items in a large database is challenging due to multiple simultaneous hypotheses and the ability to select weak associations that are statistically but not clinically significant. The simple application of the 2 test among all possible pairs of items results in mostly inappropriate associations surpassing the traditional (alpha =.05, chi(2) = 3.94) threshold. One can choose a stricter threshold to find stronger associations, but the choice may be arbitrary. We combined the volume test of Diaconis and Efron with 2 a p-value plot to select a more rigorous and less arbitrary threshold. The volume test adjusts the p-value of the Z(2) -statistic. A plot of adjusted p-values (1-p versus N-p), where N-p is the number of test statistics with a p-value greater than p, should be linear if there are no true associations. The point where the plot deviates from a line can be used as a threshold. We used linear regression to select the threshold in a reproducible fashion. In one experiment, we found that the method selected a threshold similar to that previously obtained by manually reviewing associations. (C) 2006 Elsevier Inc. All rights reserved.