A robust method for large-scale multiple hypotheses testing.
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.
Han
Seungbong
S
Department of Statistics, University of Wisconsin-Madison, Medical Science Center 1300 University Avenue, Madison, WI 53706, USA.
Andrei
Adin-Cristian
AC
Tsui
Kam-Wah
KW
D000465:Algorithms; D001699:Biometry / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D008954:Models, Biological; D015233:Models, Statistical
Bioinformatics. 2004 Jul 22;20(11):1737-45
14988112
Stat Med. 1988 Oct;7(10):1031-43
3206001
BMC Bioinformatics. 2006 Feb 01;7:50
16451725
Bioinformatics. 2004 Nov 1;20(16):2694-701
15145810
Biostatistics. 2004 Apr;5(2):155-76
15054023
Proc Natl Acad Sci U S A. 2003 Aug 5;100(16):9440-5
12883005
Genome Res. 2008 May;18(5):706-16
18347327
Bioinformatics. 2003 Jul 1;19(10):1236-42
12835267
Bioinformatics. 2006 Aug 15;22(16):1979-87
16777905
Bioinformatics. 2008 Apr 1;24(7):943-9
18245123
PLoS Genet. 2008 Apr 11;4(4):e1000052
18404217