Blakesley Richard E, Mazumdar Sati, Dew Mary Amanda, Houck Patricia R, Tang Gong, Reynolds Charles F, Butters Meryl A
Department of Biostatistics, University of Pittsburgh, PA, USA.
Neuropsychology. 2009 Mar;23(2):255-64. doi: 10.1037/a0012850.
Hypothesis testing with multiple outcomes requires adjustments to control Type I error inflation, which reduces power to detect significant differences. Maintaining the prechosen Type I error level is challenging when outcomes are correlated. This problem concerns many research areas, including neuropsychological research in which multiple, interrelated assessment measures are common. Standard p value adjustment methods include Bonferroni-, Sidak-, and resampling-class methods. In this report, the authors aimed to develop a multiple hypothesis testing strategy to maximize power while controlling Type I error. The authors conducted a sensitivity analysis, using a neuropsychological dataset, to offer a relative comparison of the methods and a simulation study to compare the robustness of the methods with respect to varying patterns and magnitudes of correlation between outcomes. The results lead them to recommend the Hochberg and Hommel methods (step-up modifications of the Bonferroni method) for mildly correlated outcomes and the step-down minP method (a resampling-based method) for highly correlated outcomes. The authors note caveats regarding the implementation of these methods using available software.
对多个结果进行假设检验需要进行调整以控制I型错误膨胀，这会降低检测显著差异的功效。当结果相关时，维持预先选定的I型错误水平具有挑战性。这个问题涉及许多研究领域，包括神经心理学研究，在该领域中，多个相互关联的评估指标很常见。标准的p值调整方法包括邦费罗尼法、西达克法和重采样类方法。在本报告中，作者旨在制定一种多重假设检验策略，以在控制I型错误的同时最大化功效。作者使用一个神经心理学数据集进行了敏感性分析，以对这些方法进行相对比较，并进行了一项模拟研究，以比较这些方法在结果之间相关性的不同模式和大小方面的稳健性。结果使他们建议，对于轻度相关的结果采用霍赫贝格法和霍梅尔法（邦费罗尼法的逐步修正法），对于高度相关的结果采用逐步下调的最小p值法（一种基于重采样的方法）。作者指出了使用现有软件实施这些方法时的注意事项。
