Literature DB >> 18612128

Gauging the performance of SNPs, biomarkers, and clinical factors for predicting risk of breast cancer.

Margaret S Pepe, Holly E Janes.   

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Year:  2008        PMID： 18612128      PMCID： PMC3132154          DOI： 10.1093/jnci/djn215

Source DB:  PubMed          Journal:  J Natl Cancer Inst        ISSN： 0027-8874            Impact factor:   13.506


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  10 in total

1.  Executive Summary of The Third Report of The National Cholesterol Education Program (NCEP) Expert Panel on Detection, Evaluation, And Treatment of High Blood Cholesterol In Adults (Adult Treatment Panel III).

Authors: 
Journal:  JAMA       Date:  2001-05-16       Impact factor: 56.272

2.  Therapeutic decision making: a cost-benefit analysis.

Authors:  S G Pauker; J P Kassirer
Journal:  N Engl J Med       Date:  1975-07-31       Impact factor: 91.245

3.  Clinical usefulness of very high and very low levels of C-reactive protein across the full range of Framingham Risk Scores.

Authors:  Paul M Ridker; Nancy Cook
Journal:  Circulation       Date:  2004-03-29       Impact factor: 29.690

4.  On criteria for evaluating models of absolute risk.

Authors:  Mitchell H Gail; Ruth M Pfeiffer
Journal:  Biostatistics       Date:  2005-04       Impact factor: 5.899

5.  Letter by Pepe et al regarding article, "Use and misuse of the receiver operating characteristic curve in risk prediction".

Authors:  Margaret S Pepe; Holly Janes; Jessie Wen Gu
Journal:  Circulation       Date:  2007-08-07       Impact factor: 29.690

6.  Integrating the predictiveness of a marker with its performance as a classifier.

Authors:  Margaret S Pepe; Ziding Feng; Ying Huang; Gary Longton; Ross Prentice; Ian M Thompson; Yingye Zheng
Journal:  Am J Epidemiol       Date:  2007-11-02       Impact factor: 4.897

7.  Evaluating the predictiveness of a continuous marker.

Authors:  Ying Huang; Margaret Sullivan Pepe; Ziding Feng
Journal:  Biometrics       Date:  2007-05-08       Impact factor: 2.571

8.  Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond.

Authors:  Michael J Pencina; Ralph B D'Agostino; Ralph B D'Agostino; Ramachandran S Vasan
Journal:  Stat Med       Date:  2008-01-30       Impact factor: 2.373

9.  Use and misuse of the receiver operating characteristic curve in risk prediction.

Authors:  Nancy R Cook
Journal:  Circulation       Date:  2007-02-20       Impact factor: 29.690

10.  Discriminatory accuracy from single-nucleotide polymorphisms in models to predict breast cancer risk.

Authors:  Mitchell H Gail
Journal:  J Natl Cancer Inst       Date:  2008-07-08       Impact factor: 13.506
  10 in total
  38 in total

1.  Inclusion of gene-gene and gene-environment interactions unlikely to dramatically improve risk prediction for complex diseases.

Authors:  Hugues Aschard; Jinbo Chen; Marilyn C Cornelis; Lori B Chibnik; Elizabeth W Karlson; Peter Kraft
Journal:  Am J Hum Genet       Date:  2012-05-24       Impact factor: 11.025

2.  Developing a utility decision framework to evaluate predictive models in breast cancer risk estimation.

Authors:  Yirong Wu; Craig K Abbey; Xianqiao Chen; Jie Liu; David C Page; Oguzhan Alagoz; Peggy Peissig; Adedayo A Onitilo; Elizabeth S Burnside
Journal:  J Med Imaging (Bellingham)       Date:  2015-08-17

3.  Evaluating the incremental value of new biomarkers with integrated discrimination improvement.

Authors:  Kathleen F Kerr; Robyn L McClelland; Elizabeth R Brown; Thomas Lumley
Journal:  Am J Epidemiol       Date:  2011-06-14       Impact factor: 4.897

4.  Measures to summarize and compare the predictive capacity of markers.

Authors:  Wen Gu; Margaret Pepe
Journal:  Int J Biostat       Date:  2009-10-01       Impact factor: 0.968

5.  Fine-mapping of breast cancer susceptibility loci characterizes genetic risk in African Americans.

Authors:  Fang Chen; Gary K Chen; Robert C Millikan; Esther M John; Christine B Ambrosone; Leslie Bernstein; Wei Zheng; Jennifer J Hu; Regina G Ziegler; Sandra L Deming; Elisa V Bandera; Sarah Nyante; Julie R Palmer; Timothy R Rebbeck; Sue A Ingles; Michael F Press; Jorge L Rodriguez-Gil; Stephen J Chanock; Loïc Le Marchand; Laurence N Kolonel; Brian E Henderson; Daniel O Stram; Christopher A Haiman
Journal:  Hum Mol Genet       Date:  2011-08-18       Impact factor: 6.150

6.  Evaluating health risk models.

Authors:  Alice S Whittemore
Journal:  Stat Med       Date:  2010-10-15       Impact factor: 2.373

7.  Assessing the discriminative ability of risk models for more than two outcome categories.

Authors:  Ben Van Calster; Yvonne Vergouwe; Caspar W N Looman; Vanya Van Belle; Dirk Timmerman; Ewout W Steyerberg
Journal:  Eur J Epidemiol       Date:  2012-10-07       Impact factor: 8.082

8.  The potential for using risk models in future lung cancer screening trials.

Authors:  John K Field; Olaide Y Raji
Journal:  F1000 Med Rep       Date:  2010-05-24

9.  A unifying framework for evaluating the predictive power of genetic variants based on the level of heritability explained.

Authors:  Hon-Cheong So; Pak C Sham
Journal:  PLoS Genet       Date:  2010-12-02       Impact factor: 5.917

10.  Genome-based prediction of common diseases: methodological considerations for future research.

Authors:  A Cecile Jw Janssens; Cornelia M van Duijn
Journal:  Genome Med       Date:  2009-02-18       Impact factor: 11.117
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