![]() The FDR approach allows more claims of significant differences to be made, provided the investigator is willing to accept a small fraction of false-positive findings. We show how to use a collection of such p-values to estimate the number of true null hypotheses m0 among the m null hypotheses tested and how to estimate the false discovery rate (FDR) associated with p-value significance thresholds. When a study involves a large number of tests, the FDR error measure is a more useful approach to determining a significance cutoff, as the FWE approach is too stringent. Each sequential permutation p-value has a null distribution that is nonuniform on a discrete support. We show that if the original procedure has a type I error guarantee in a certain family (including FDR and FWER), then the sequential conversion inherits an. The most straightforward method for controlling the FDR makes an assumption of independence between tests, while other FDR-controlling methods make less. In multiple testing, a classical "family-wise error rate" (FWE) approach is commonly used when the number of tests is small. An FDR-controlling procedure is described and illustrated with a numerical example. The FDR is the expected proportion of the null hypotheses that are falsely rejected divided by the total number of rejections. This paper presents an overview of the multiple testing framework and describes the false discovery rate (FDR) approach to determining the significance cutoff when a large number of tests are conducted. When more than one test is conducted, use of a significance level intended for use by a single test typically leads to a large chance of false-positive findings. The null hypothesis is rejected and a discovery is declared when the P value is less than a prespecified significance level. advanced sequential testing tools in 2015 to safeguard their users from making such false. Typically, a test statistic and its corresponding P value are calculated to measure the extent of the difference between the two groups. increases the false discovery rate (FDR) from 33 to 42 among. A simple sequential Bonferronitype procedure is proved to control the false discovery rate for independent test statistics, and a simulation study shows. We prove the procedure's error control and give some tips for implementation in commonly encountered testing situations.Ħ2J15 62L10 Generalized likelihood ratio Multiple comparisons Multiple endpoint clinical trials Multiple testing Sequential analysis Sequential hypothesis testing Wald approximations.In experimental research, a statistical test is often used for making decisions on a null hypothesis such as that the means of gene expression in the normal and tumor groups are equal. The procedure is a natural extension of Benjamini and Hochberg's (1995) widely-used fixed sample size procedure to the domain of sequential data, with the added benefit of simultaneous FDR and FNR control that sequential sampling affords. our main result, given in theorem 3. The procedure can be used with sequential, group sequential, truncated, or other sampling schemes. The false discovery rate (FDR), which was introduced by Benjamini and Hochberg (1995), has become the error criterion of choice for large-scale multiple. All that is needed is a test statistic for each data stream that controls its conventional type I and II error probabilities, and no information or assumptions are required about the joint distribution of the statistics or data streams. the false discovery rate (FDR) False Discovery Rate m 0 m-m 0 m V S R Called Significant U T m - R Not Called Significant. We propose a general and flexible procedure for testing multiple hypotheses about sequential (or streaming) data that simultaneously controls both the false discovery rate (FDR) and false nondiscovery rate (FNR) under minimal assumptions about the data streams which may differ in distribution, dimension, and be dependent. Storeys work is closely related to Benjamini and Hochbergs (1995) landmark paper on the false discovery rate (FDR). Moreover, the online decision-making process may come to a halt when the total error budget, or alpha-wealth, is exhausted.
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