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Nonparametric estimation of the rediscovery rate
Author(s) -
Lee Donghwan,
Ganna Andrea,
Pawitan Yudi,
Lee Woojoo
Publication year - 2016
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/sim.6915
Subject(s) - nonparametric statistics , false positive paradox , false discovery rate , reliability (semiconductor) , statistics , computer science , multiple comparisons problem , reproducibility , estimation , econometrics , data mining , mathematics , biology , power (physics) , biochemistry , physics , quantum mechanics , gene , management , economics
Validation studies have been used to increase the reliability of the statistical conclusions for scientific discoveries; such studies improve the reproducibility of the findings and reduce the possibility of false positives. Here, one of the important roles of statistics is to quantify reproducibility rigorously. Two concepts were recently defined for this purpose: (i) rediscovery rate (RDR), which is the expected proportion of statistically significant findings in a study that can be replicated in the validation study and (ii) false discovery rate in the validation study (vFDR). In this paper, we aim to develop a nonparametric approach to estimate the RDR and vFDR and show an explicit link between the RDR and the FDR. Among other things, the link explains why reproducing statistically significant results even with low FDR level may be difficult. Two metabolomics datasets are considered to illustrate the application of the RDR and vFDR concepts in high‐throughput data analysis. Copyright © 2016 John Wiley & Sons, Ltd.