Document Detail


A robust method for large-scale multiple hypotheses testing.
MedLine Citation:
PMID:  20391535     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
When drawing large-scale simultaneous inference, such as in genomics and imaging problems, multiplicity adjustments should be made, since, otherwise, one would be faced with an inflated type I error. Numerous methods are available to estimate the proportion of true null hypotheses pi(0), among a large number of hypotheses tested. Many methods implicitly assume that the pi(0) is large, that is, close to 1. However, in practice, mid-range pi(0) values are frequently encountered and many of the widely used methods tend to produce highly variable or biased estimates of pi(0). As a remedy in such situations, we propose a hierarchical Bayesian model that produces an estimator of pi(0) that exhibits considerably less bias and is more stable. Simulation studies seem indicative of good method performance even when low-to-moderate correlation exists among test statistics. Method performance is assessed in simulated settings and its practical usefulness is illustrated in an application to a type II diabetes study.
Authors:
Seungbong Han; Adin-Cristian Andrei; Kam-Wah Tsui
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Publication Detail:
Type:  Journal Article; Research Support, N.I.H., Extramural; Research Support, U.S. Gov't, Non-P.H.S.    
Journal Detail:
Title:  Biometrical journal. Biometrische Zeitschrift     Volume:  52     ISSN:  1521-4036     ISO Abbreviation:  Biom J     Publication Date:  2010 Apr 
Date Detail:
Created Date:  2010-05-03     Completed Date:  2010-09-13     Revised Date:  2014-04-25    
Medline Journal Info:
Nlm Unique ID:  7708048     Medline TA:  Biom J     Country:  Germany    
Other Details:
Languages:  eng     Pagination:  222-32     Citation Subset:  IM    
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MeSH Terms
Descriptor/Qualifier:
Algorithms*
Biometry / methods*
Computer Simulation
Data Interpretation, Statistical*
Models, Biological*
Models, Statistical*
Grant Support
ID/Acronym/Agency:
P30 CA014520-36/CA/NCI NIH HHS; R21 CA132267/CA/NCI NIH HHS; R21 CA132267-02/CA/NCI NIH HHS; UL1 RR025011/RR/NCRR NIH HHS; UL1 RR025011-03/RR/NCRR NIH HHS; W81XWH-08-1-0341//PHS HHS
Comments/Corrections

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