摘要
Efron (1997) considered several approximations of p-values for simultaneous hypothesis testing. An extension of his approaches is considered here to approximate various probabilities of correlated events. Compared with multiple-integrations, our proposed method, the parallelogram formulas, based on a one-dimensional integral, not only substantially reduces the computational complexity but also maintains good accuracy. Applications of the proposed method to genetic association studies and group sequential analysis are investigated in detail. Numerical results including real data analysis and simulation studies demonstrate that the proposed method performs well.
Efron (1997) considered several approximations of p-values for simultaneous hypothesis testing. An extension of his approaches is considered here to approximate various probabilities of correlated events. Compared with multiple-integrations, our proposed method, the parallelogram formulas, based on a one-dimensional integral, not only substantially reduces the computational complexity but also maintains good accuracy. Applications of the proposed method to genetic association studies and group sequential analysis are investigated in detail. Numerical results including real data analysis and simulation studies demonstrate that the proposed method performs well.
作者
LI QiZhai1,2, ZHENG Gang3, LIU AiYi4, LI ZhaoHai2,5 & YU Kai2 1Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
2Biostatistics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA
3Office of Biostatistics Research, National Heart, Lung and Blood Institute, Bethesda, MD 20892, USA
4Biostatistics and Bioinformatics Branch, National Institute of Child Health and Human Development, Bethesda, MD 20892, USA
5Department of Statistics, George Washington University, Washington, DC 20052, USA
基金
supported by the Intramural Program of NIH
supported in part by National Natural Science Foundation of China (Grant No.10901155)
supportedin part by NIH (Grant No. EY014478).
