Document Detail

Ranking prognosis markers in cancer genomic studies.
MedLine Citation:
PMID:  21087949     Owner:  NLM     Status:  MEDLINE    
In cancer research, high-throughput genomic studies have been extensively conducted, searching for markers associated with cancer diagnosis, prognosis and variation in response to treatment. In this article, we analyze cancer prognosis studies and investigate ranking markers based on their marginal prognosis power. To avoid ambiguity, we focus on microarray gene expression studies where genes are the markers, but note that the methodology and results are applicable to other high-throughput studies. The objectives of this study are 2-fold. First, we investigate ranking markers under three commonly adopted semiparametric models, namely the Cox, accelerated failure time and additive risk models. Data analysis shows that the ranking may vary significantly under different models. Second, we describe a nonparametric concordance measure, which has roots in the time-dependent ROC (receiver operating characteristic) framework and relies on much weaker assumptions than the semiparametric models. In simulation, it is shown that ranking using the concordance measure is not sensitive to model specification whereas ranking under the semiparametric models is. In data analysis, the concordance measure generates rankings significantly different from those under the semiparametric models.
Shuangge Ma; Xiao Song
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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.     Date:  2010-11-18
Journal Detail:
Title:  Briefings in bioinformatics     Volume:  12     ISSN:  1477-4054     ISO Abbreviation:  Brief. Bioinformatics     Publication Date:  2011 Jan 
Date Detail:
Created Date:  2011-01-31     Completed Date:  2011-06-15     Revised Date:  2013-07-03    
Medline Journal Info:
Nlm Unique ID:  100912837     Medline TA:  Brief Bioinform     Country:  England    
Other Details:
Languages:  eng     Pagination:  33-40     Citation Subset:  IM    
Yale University, New Haven, CT 06520, USA.
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MeSH Terms
Gene Expression Profiling / methods*
Neoplasms / diagnosis*,  genetics
Oligonucleotide Array Sequence Analysis / methods*
ROC Curve
Grant Support

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine

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