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Evaluations of FDR-controlling procedures in multiple hypothesis testing

机译:多重假设检验中FDR控制程序的评估

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Many exploratory experiments such as DNA mi-croarray or brain imaging require simultaneously comparisons of hundreds or thousands of hypotheses. Under such a setting, using the false discovery rate (FDR) as an overall Type I error is recommended (Benjamini and Hochberg in J. R. Stat. Soc. B 57:289-300,1995). Many FDR controlling procedures have been proposed. However, when evaluating the performance of FDR-controlling procedures, researchers are often focused on the ability of procedures to control the FDR and to achieve high power. Meanwhile, under the multiple hypotheses, it may be also likely to commit a false non-discovery or fail to claim a true non-significance. In addition, various experimental parameters such as the number of hypotheses, the proportion of the number of true null hypotheses to the number of hypotheses, the samples size and the correlation structure may affect the performance of FDR controlling procedures. The purpose of this paper is to illustrate the performance of some existing FDR controlling procedures in terms of four indices, i.e., the FDR, the false non-discovery rate, the sensitivity and the specificity. Analytical results of these indices for the FDR controlling procedures are derived. Simulations are also performed to evaluate the performance of controlling procedures in terms of these indices under various experimental parameters. The result can be used to summarize as a guidance for practitioners to properly choose a FDR controlling procedure.
机译:许多探索性实验(例如DNA微阵列或脑成像)需要同时比较数百或数千个假设。在这种情况下,建议使用错误发现率(FDR)作为整体I型错误(Benjamini和Hochberg,J。R. Stat。Soc。B 57:289-300,1995)。已经提出了许多FDR控制程序。但是,在评估FDR控制程序的性能时,研究人员通常将重点放在程序控制FDR和获得高功率的能力上。同时,在多种假设下,它也可能会做出错误的未发现或未能主张真正的不重要。此外,各种实验参数,例如假设数量,真实无效假设数量与假设数量的比例,样本大小和相关结构,都可能影响FDR控制程序的性能。本文的目的是根据FDR,错误的未发现率,敏感性和特异性这四个指标来说明一些现有的FDR控制程序的性能。得出了FDR控制程序的这些指标的分析结果。还进行了仿真以根据各种实验参数下的这些指标评估控制程序的性能。结果可以总结为从业人员正确选择FDR控制程序的指南。

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