Efficient Stratified Testing Procedure for a False Discovery Rate |
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Authors: | Seungbong Han Adin-Cristian Andrei Kam-Wah Tsui |
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Institution: | 1. Department of Clinical Epidemiology and Biostatistics, University of Ulsan College of Medicine, Asan Medical Center, Songpa-gu, Seoul, Korea;2. BCVI Clinical Trials Unit, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA;3. Department of Statistics, University of Wisconsin-Madison, Madison, WI, USA |
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Abstract: | The false discovery rate (FDR) has become a popular error measure in the large-scale simultaneous testing. When data are collected from heterogenous sources and form grouped hypotheses testing, it may be beneficial to use the distinct feature of groups to conduct the multiple hypotheses testing. We propose a stratified testing procedure that uses different FDR levels according to the stratification features based on p-values. Our proposed method is easy to implement in practice. Simulations studies show that the proposed method produces more efficient testing results. The stratified testing procedure minimizes the overall false negative rate (FNR) level, while controlling the overall FDR. An example from a type II diabetes mice study further illustrates the practical advantages of this new approach. |
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Keywords: | Gene expression Microarray data Multiple testing Stratified testing |
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