Conjunction group analysis: An alternative to mixed/random effect analysis

被引:47
作者
Heller, Ruth
Golland, Yulia
Malach, Rafael
Benjamini, Yoav [1 ]
机构
[1] Tel Aviv Univ, Sackler Fac Exact Sci, Dept Stat & Operat Res, IL-69978 Tel Aviv, Israel
[2] Hebrew Univ Jerusalem, Dept Psychol, IL-91905 Jerusalem, Israel
[3] Weizmann Inst Sci, Dept Neurobiol, IL-76100 Rehovot, Israel
关键词
combining tests; dependent contrasts; false discovery rate; global null hypothesis; meta-analysis; multiple comparisons; pooled significance values;
D O I
10.1016/j.neuroimage.2007.05.051
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
We address the problem of testing in every brain voxel v whether at least u out of n conditions (or subjects) considered shows a real effect. The only statistic suggested so far, the maximum p-value method, fails under dependency (unless u=n) and in particular under positive dependency that arises if all stimuli are compared to the same control stimulus. Moreover, it tends to have low power under independence. For testing that at least u out of n conditions shows a real effect, we suggest powerful test statistics that are valid under dependence between the individual condition p-values as well as under independence and other test statistics that are valid under independence. We use the above approach, replacing conditions by subjects, to produce informative group maps and thereby offer an alternative to mixed/random effect analysis. (c) 2007 Elsevier Inc. All rights reserved.
引用
收藏
页码:1178 / 1185
页数:8
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