Literature DB >> 21571811

Commentary: Reporting standards are needed for evaluations of risk reclassification.

Margaret S Pepe1, Holly Janes.   

Abstract

Mesh：

Year:  2011        PMID： 21571811      PMCID： PMC3156371          DOI： 10.1093/ije/dyr083

Source DB:  PubMed          Journal:  Int J Epidemiol        ISSN： 0300-5771            Impact factor:   7.196


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  10 in total

1.  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

2.  Problems with risk reclassification methods for evaluating prediction models.

Authors:  Margaret S Pepe
Journal:  Am J Epidemiol       Date:  2011-05-09       Impact factor: 4.897

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.  Use of reclassification for assessment of improved prediction: an empirical evaluation.

Authors:  Ioanna Tzoulaki; George Liberopoulos; John P A Ioannidis
Journal:  Int J Epidemiol       Date:  2011-02-16       Impact factor: 7.196

5.  Extensions of net reclassification improvement calculations to measure usefulness of new biomarkers.

Authors:  Michael J Pencina; Ralph B D'Agostino; Ewout W Steyerberg
Journal:  Stat Med       Date:  2010-11-05       Impact factor: 2.373

6.  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

7.  Advances in measuring the effect of individual predictors of cardiovascular risk: the role of reclassification measures.

Authors:  Nancy R Cook; Paul M Ridker
Journal:  Ann Intern Med       Date:  2009-06-02       Impact factor: 25.391

8.  Assessing the value of risk predictions by using risk stratification tables.

Authors:  Holly Janes; Margaret S Pepe; Wen Gu
Journal:  Ann Intern Med       Date:  2008-11-18       Impact factor: 25.391

9.  Strengthening the reporting of Genetic RIsk Prediction Studies: the GRIPS Statement.

Authors:  A Cecile J W Janssens; John P A Ioannidis; Cornelia M van Duijn; Julian Little; Muin J Khoury
Journal:  PLoS Med       Date:  2011-03-15       Impact factor: 11.069

10.  One statistical test is sufficient for assessing new predictive markers.

Authors:  Andrew J Vickers; Angel M Cronin; Colin B Begg
Journal:  BMC Med Res Methodol       Date:  2011-01-28       Impact factor: 4.615
  10 in total
  11 in total

1.  Further insight into the incremental value of new markers: the interpretation of performance measures and the importance of clinical context.

Authors:  Kathleen F Kerr; Aasthaa Bansal; Margaret S Pepe
Journal:  Am J Epidemiol       Date:  2012-08-08       Impact factor: 4.897

2.  Clinically relevant measures of fit? A note of caution.

Authors:  Nancy R Cook
Journal:  Am J Epidemiol       Date:  2012-08-08       Impact factor: 4.897

Review 3.  Key concepts and limitations of statistical methods for evaluating biomarkers of kidney disease.

Authors:  Chirag R Parikh; Heather Thiessen-Philbrook
Journal:  J Am Soc Nephrol       Date:  2014-05-01       Impact factor: 10.121

4.  Risk of poor outcomes with novel and traditional biomarkers at clinical AKI diagnosis.

Authors:  Isaac E Hall; Steven G Coca; Mark A Perazella; Umo U Eko; Randy L Luciano; Patricia R Peter; Won K Han; Chirag R Parikh
Journal:  Clin J Am Soc Nephrol       Date:  2011-10-27       Impact factor: 8.237

5.  Development and validation of a cardiovascular disease risk-prediction model using population health surveys: the Cardiovascular Disease Population Risk Tool (CVDPoRT).

Authors:  Douglas G Manuel; Meltem Tuna; Carol Bennett; Deirdre Hennessy; Laura Rosella; Claudia Sanmartin; Jack V Tu; Richard Perez; Stacey Fisher; Monica Taljaard
Journal:  CMAJ       Date:  2018-07-23       Impact factor: 8.262

6.  Development and validation of a coronary risk prediction model for older U.S. and European persons in the Cardiovascular Health Study and the Rotterdam Study.

Authors:  Michael T Koller; Maarten J G Leening; Marcel Wolbers; Ewout W Steyerberg; M G Myriam Hunink; Rotraut Schoop; Albert Hofman; Heiner C Bucher; Bruce M Psaty; Donald M Lloyd-Jones; Jacqueline C M Witteman
Journal:  Ann Intern Med       Date:  2012-09-18       Impact factor: 25.391

7.  Quantifying the value of biomarkers for predicting mortality.

Authors:  Noreen Goldman; Dana A Glei
Journal:  Ann Epidemiol       Date:  2015-08-29       Impact factor: 3.797

8.  How to improve the performance of intraoperative risk models: an example with vital signs using the surgical apgar score.

Authors:  Joseph A Hyder; Daryl J Kor; Robert R Cima; Arun Subramanian
Journal:  Anesth Analg       Date:  2013-12       Impact factor: 5.108

9.  Complex signals bioinformatics: evaluation of heart rate characteristics monitoring as a novel risk marker for neonatal sepsis.

Authors:  Douglas E Lake; Karen D Fairchild; J Randall Moorman
Journal:  J Clin Monit Comput       Date:  2013-11-19       Impact factor: 2.502

10.  Risk reclassification analysis investigating the added value of fatigue to sickness absence predictions.

Authors:  Corné A M Roelen; Ute Bültmann; Johan W Groothoff; Jos W R Twisk; Martijn W Heymans
Journal:  Int Arch Occup Environ Health       Date:  2015-02-22       Impact factor: 3.015
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