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자연과학대학
통계학전공
Journal papers
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Overall assessment for selected markers from high-throughput data
Title
Overall assessment for selected markers from high-throughput data
Authors
Lee W.
;
Lee D.
;
Pawitan Y.
Ewha Authors
이동환
SCOPUS Author ID
이동환
Issue Date
2022
Journal Title
Statistics in Medicine
ISSN
0277-6715
Citation
Statistics in Medicine vol. 41, no. 30, pp. 5830 - 5843
Keywords
false discovery rate
;
reproducibility
;
selection adjustment
;
validation study
Publisher
John Wiley and Sons Ltd
Indexed
SCIE; SCOPUS
Document Type
Article
Abstract
Reproducibility, a hallmark of science, is typically assessed in validation studies. We focus on high-throughput studies where a large number of biomarkers is measured in a training study, but only a subset of the most significant findings is selected and re-tested in a validation study. Our aim is to get the statistical measures of overall assessment for the selected markers, by integrating the information in both the training and validation studies. Naive statistical measures, such as the combined (Formula presented.) -value by conventional meta-analysis, that ignore the non-random selection are clearly biased, producing over-optimistic significance. We use the false-discovery rate (FDR) concept to develop a selection-adjusted FDR (sFDR) as an overall assessment measure. We describe the link between the overall assessment and other concepts such as replicability and meta-analysis. Some simulation studies and two real metabolomic datasets are considered to illustrate the application of sFDR in high-throughput data analyses. © 2022 John Wiley & Sons Ltd.
DOI
10.1002/sim.9596
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