Literature DB >> 24790177

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

Chirag R Parikh1, Heather Thiessen-Philbrook2.   

Abstract

Interest in developing and using novel markers of kidney injury is increasing. To maintain scientific rigour in these endeavors, a comprehensive understanding of statistical methodology is required to rigorously assess the incremental value of novel biomarkers in existing clinical risk prediction models. Such knowledge is especially relevant, because no single statistical method is sufficient to evaluate a novel biomarker. In this review, we highlight the strengths and limitations of various traditional and novel statistical methods used in the literature for biomarker studies and use biomarkers of AKI as examples to show limitations of some popular statistical methods.
Copyright © 2014 by the American Society of Nephrology.

Entities:  

Keywords:  NRI; biomarker; c index

Mesh：

Substances：

Year:  2014        PMID： 24790177      PMCID： PMC4116071          DOI： 10.1681/ASN.2013121300

Source DB:  PubMed          Journal:  J Am Soc Nephrol        ISSN： 1046-6673            Impact factor:   10.121


  36 in total

Review 1.  Phases of biomarker development for early detection of cancer.

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Journal:  J Natl Cancer Inst       Date:  2001-07-18       Impact factor: 13.506

2.  New metrics for assessing diagnostic potential of candidate biomarkers.

Authors:  John W Pickering; Zoltan H Endre
Journal:  Clin J Am Soc Nephrol       Date:  2012-06-07       Impact factor: 8.237

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

Authors:  Margaret S Pepe; Holly Janes
Journal:  Int J Epidemiol       Date:  2011-05-13       Impact factor: 7.196

4.  Assessing the incremental value of diagnostic and prognostic markers: a review and illustration.

Authors:  Ewout W Steyerberg; Michael J Pencina; Hester F Lingsma; Michael W Kattan; Andrew J Vickers; Ben Van Calster
Journal:  Eur J Clin Invest       Date:  2011-07-05       Impact factor: 4.686

5.  Interpreting incremental value of markers added to risk prediction models.

Authors:  Michael J Pencina; Ralph B D'Agostino; Karol M Pencina; A Cecile J W Janssens; Philip Greenland
Journal:  Am J Epidemiol       Date:  2012-08-08       Impact factor: 4.897

Review 6.  Net reclassification improvement: computation, interpretation, and controversies: a literature review and clinician's guide.

Authors:  Maarten J G Leening; Moniek M Vedder; Jacqueline C M Witteman; Michael J Pencina; Ewout W Steyerberg
Journal:  Ann Intern Med       Date:  2014-01-21       Impact factor: 25.391

7.  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

8.  Testing for improvement in prediction model performance.

Authors:  Margaret Sullivan Pepe; Kathleen F Kerr; Gary Longton; Zheyu Wang
Journal:  Stat Med       Date:  2013-01-07       Impact factor: 2.373

Review 9.  Biomarkers for the diagnosis and risk stratification of acute kidney injury: a systematic review.

Authors:  S G Coca; R Yalavarthy; J Concato; C R Parikh
Journal:  Kidney Int       Date:  2007-12-19       Impact factor: 10.612

Review 10.  Net reclassification indices for evaluating risk prediction instruments: a critical review.

Authors:  Kathleen F Kerr; Zheyu Wang; Holly Janes; Robyn L McClelland; Bruce M Psaty; Margaret S Pepe
Journal:  Epidemiology       Date:  2014-01       Impact factor: 4.822
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  20 in total

1.  The effects of performance status one week before hospital admission on the outcomes of critically ill patients.

Authors:  Fernando G Zampieri; Fernando A Bozza; Giulliana M Moralez; Débora D S Mazza; Alexandre V Scotti; Marcelo S Santino; Rubens A B Ribeiro; Edison M Rodrigues Filho; Maurício M Cabral; Marcelo O Maia; Patrícia S D'Alessandro; Sandro V Oliveira; Márcia A M Menezes; Eliana B Caser; Roberto S Lannes; Meton S Alencar Neto; Maristela M Machado; Marcelo F Sousa; Jorge I F Salluh; Marcio Soares
Journal:  Intensive Care Med       Date:  2016-09-29       Impact factor: 17.440

2.  Urinary C-X-C Motif Chemokine 10 Independently Improves the Noninvasive Diagnosis of Antibody-Mediated Kidney Allograft Rejection.

Authors:  Marion Rabant; Lucile Amrouche; Xavier Lebreton; Florence Aulagnon; Aurélien Benon; Virginia Sauvaget; Raja Bonifay; Lise Morin; Anne Scemla; Marianne Delville; Frank Martinez; Marc Olivier Timsit; Jean-Paul Duong Van Huyen; Christophe Legendre; Fabiola Terzi; Dany Anglicheau
Journal:  J Am Soc Nephrol       Date:  2015-05-06       Impact factor: 10.121

3.  Associations between Deceased-Donor Urine Injury Biomarkers and Kidney Transplant Outcomes.

Authors:  Peter P Reese; Isaac E Hall; Francis L Weng; Bernd Schröppel; Mona D Doshi; Rick D Hasz; Heather Thiessen-Philbrook; Joseph Ficek; Veena Rao; Patrick Murray; Haiqun Lin; Chirag R Parikh
Journal:  J Am Soc Nephrol       Date:  2015-09-15       Impact factor: 10.121

4.  Preoperative NT-proBNP and LVEF for the prediction of acute kidney injury after noncardiac surgery: a single-centre retrospective study.

Authors:  Jiaqi Wang; Yehong Dong; Bingcheng Zhao; Kexuan Liu
Journal:  BMC Anesthesiol       Date:  2022-06-24       Impact factor: 2.376

5.  Utility of Biomarkers to Improve Prediction of Readmission or Mortality After Cardiac Surgery.

Authors:  Jeremiah R Brown; Jeffrey P Jacobs; Shama S Alam; Heather Thiessen-Philbrook; Allen Everett; Donald S Likosky; Kevin Lobdell; Moritz C Wyler von Ballmoos; Devin M Parker; Amit X Garg; Todd Mackenzie; Marshall L Jacobs; Chirag R Parikh
Journal:  Ann Thorac Surg       Date:  2018-08-04       Impact factor: 4.330

Review 6.  Lipidomics and Biomarker Discovery in Kidney Disease.

Authors:  Farsad Afshinnia; Thekkelnaycke M Rajendiran; Stefanie Wernisch; Tanu Soni; Adil Jadoon; Alla Karnovsky; George Michailidis; Subramaniam Pennathur
Journal:  Semin Nephrol       Date:  2018-03       Impact factor: 5.299

7.  Predicting survival after liver transplantation in patients with hepatocellular carcinoma using the LiTES-HCC score.

Authors:  David Goldberg; Alejandro Mantero; Craig Newcomb; Cindy Delgado; Kimberly A Forde; David E Kaplan; Binu John; Nadine Nuchovich; Barbara Dominguez; Ezekiel Emanuel; Peter P Reese
Journal:  J Hepatol       Date:  2021-01-13       Impact factor: 30.083

8.  The serum heart-type fatty acid-binding protein (HFABP) levels can be used to detect the presence of acute kidney injury on admission in patients admitted to the non-surgical intensive care unit.

Authors:  Akihiro Shirakabe; Nobuaki Kobayashi; Noritake Hata; Takuro Shinada; Kazunori Tomita; Masafumi Tsurumi; Hirotake Okazaki; Masato Matsushita; Yoshiya Yamamoto; Shinya Yokoyama; Kuniya Asai; Wataru Shimizu
Journal:  BMC Cardiovasc Disord       Date:  2016-09-05       Impact factor: 2.298

9.  The fatty liver index as a predictor of incident chronic kidney disease in a 10-year prospective cohort study.

Authors:  Ji Hye Huh; Jang Young Kim; Eunhee Choi; Jae Seok Kim; Yoosoo Chang; Ki-Chul Sung
Journal:  PLoS One       Date:  2017-07-24       Impact factor: 3.240

10.  Developing renal allograft surveillance strategies - urinary biomarkers of cellular rejection.

Authors:  Patricia Hirt-Minkowski; Sacha A De Serres; Julie Ho
Journal:  Can J Kidney Health Dis       Date:  2015-08-18
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