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題名 Comparison of methods for estimating the number of true null hypotheses in mulitplicity testing
作者 Hsueh, Huey-Miin ;Chen J. J.;Kodel R. L.
薛慧敏
日期 2003
上傳時間 19-Dec-2008 14:52:55 (UTC+8)
摘要 When a large number of statistical tests is performed, the chance of false positive findings could increase considerably. The traditional approach is to control the probability of rejecting at least one true null hypothesis, the familywise error rate (FWE). To improve the power of detecting treatment differences, an alternative approach is to control the expected proportion of errors among the rejected hypotheses, the false discovery rate (FDR). When some of the hypotheses are not true, the error rate from either the FWE- or the FDR-controlling procedure is usually lower than the designed level. This paper compares five methods used to estimate the number of true null hypotheses over a large number of hypotheses. The estimated number of true null hypotheses is then used to improve the power of FWE- or FDR-controlling methods. Monte Carlo simulations are conducted to evaluate the performance of these methods. The lowest slope method, developed by Benjamini and Hochberg (2000) on the adaptive control of the FDR in multiple testing with independent statistics, and the mean of differences method appear to perform the best. These two methods control the FWE properly when the number of nontrue null hypotheses is small. A data set from a toxicogenomic microarray experiment is used for illustration.
關聯 Journal of Biopharmaceutical Statistics, 13(4),675-689
資料類型 article
DOI http://dx.doi.org/10.1081/BIP-120024202
dc.creator (作者) Hsueh, Huey-Miin ;Chen J. J.;Kodel R. L.en_US
dc.creator (作者) 薛慧敏-
dc.date (日期) 2003en_US
dc.date.accessioned 19-Dec-2008 14:52:55 (UTC+8)-
dc.date.available 19-Dec-2008 14:52:55 (UTC+8)-
dc.date.issued (上傳時間) 19-Dec-2008 14:52:55 (UTC+8)-
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/18174-
dc.description.abstract (摘要) When a large number of statistical tests is performed, the chance of false positive findings could increase considerably. The traditional approach is to control the probability of rejecting at least one true null hypothesis, the familywise error rate (FWE). To improve the power of detecting treatment differences, an alternative approach is to control the expected proportion of errors among the rejected hypotheses, the false discovery rate (FDR). When some of the hypotheses are not true, the error rate from either the FWE- or the FDR-controlling procedure is usually lower than the designed level. This paper compares five methods used to estimate the number of true null hypotheses over a large number of hypotheses. The estimated number of true null hypotheses is then used to improve the power of FWE- or FDR-controlling methods. Monte Carlo simulations are conducted to evaluate the performance of these methods. The lowest slope method, developed by Benjamini and Hochberg (2000) on the adaptive control of the FDR in multiple testing with independent statistics, and the mean of differences method appear to perform the best. These two methods control the FWE properly when the number of nontrue null hypotheses is small. A data set from a toxicogenomic microarray experiment is used for illustration.-
dc.format application/en_US
dc.language enen_US
dc.language en-USen_US
dc.language.iso en_US-
dc.relation (關聯) Journal of Biopharmaceutical Statistics, 13(4),675-689en_US
dc.title (題名) Comparison of methods for estimating the number of true null hypotheses in mulitplicity testingen_US
dc.type (資料類型) articleen
dc.identifier.doi (DOI) 10.1081/BIP-120024202en_US
dc.doi.uri (DOI) http://dx.doi.org/10.1081/BIP-120024202en_US