title: An Integrated Bayesian Nonparametric Approach for Stochastic and Variability Orders in ROC Curve Estimation: An Application to Endometriosis Diagnosis.
abstract: In estimating ROC curves of multiple tests, somea prioriconstraints may exist, either between the healthy and diseased populations within a test or between tests within a population. In this paper, we proposed an integrated modeling approach for ROC curves that jointly accounts for stochastic and variability orders. The stochastic order constrains the distributional centers of the diseased and healthy populations within a test, while the variability order constrains the distributional spreads of the tests within each of the populations. Under a Bayesian nonparametric framework, we used features of the Dirichlet process mixture to incorporate these order constraints in a natural way. We applied the proposed approach to data from the Physician Reliability Study that investigated the accuracy of diagnosing endometriosis using different clinical information. To address the issue of no gold standard in the real data, we used a sensitivity analysis approach that exploited diagnosis from a panel of experts. To demonstrate the performance of the methodology, we conducted simulation studies with varying sample sizes, distributional assumptions and order constraints. Supplementary materials for this article are available online.
authors.affiliation: Biostatistics and Bioinformatics Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892.
authors.forename: Beom Seuk
authors.initials: BS
authors.lastname: Hwang
authors.affiliation: Biostatistics and Bioinformatics Branch, Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD 20892.
authors.forename: Zhen
authors.initials: Z
authors.lastname: Chen
keywords: Area under the curve; Dirichlet process mixture; Gold standard; Order restricted analysis
references.citation: Biostatistics. 2014 Apr;15(2):353-69
references.pmid: 24174579
references.citation: Stat Med. 2002 May 15;21(9):1213-35
references.pmid: 12111875
references.citation: Stat Med. 2010 Sep 10;29(20):2090-106
references.pmid: 20603894
references.citation: Med Decis Making. 1996 Oct-Dec;16(4):404-11
references.pmid: 8912302
references.citation: Biometrics. 2000 Jun;56(2):352-9
references.pmid: 10877289
references.citation: Stat Med. 2013 Jun 15;32(13):2209-20
references.pmid: 23055248
references.citation: Obstet Gynecol. 2012 Jul;120(1):104-12
references.pmid: 22914398
references.citation: Lifetime Data Anal. 2004 Jun;10(2):159-73
references.pmid: 15293630
references.citation: Biometrics. 2012 Dec;68(4):1294-302
references.pmid: 23006010
references.citation: Stat Med. 2012 Jan 30;31(2):131-42
references.pmid: 22139729
references.citation: Stat Med. 2008 Nov 20;27(26):5407-20
references.pmid: 18613217
references.citation: Prev Vet Med. 2005 May 10;68(2-4):145-63
references.pmid: 15820113
references.citation: Biostatistics. 2002 Sep;3(3):421-32
references.pmid: 12933607
references.citation: Biostatistics. 2004 Oct;5(4):573-86
references.pmid: 15475420
references.citation: Stat Med. 2006 Nov 30;25(22):3905-28
references.pmid: 16416403
references.citation: Stat Med. 1996 Jul 30;15(14):1575-85
references.pmid: 8855483
references.citation: Biometrics. 1998 Mar;54(1):124-35
references.pmid: 9544511
references.citation: Biometrika. 2008 Dec;95(4):859-874
references.pmid: 32148335
references.citation: Stat Med. 2002 Oct 30;21(20):3093-106
references.pmid: 12369084
references.citation: Stat Med. 2008 Jun 15;27(13):2474-96
references.pmid: 18300333