Literature DB >> 21410995

Strengthening the reporting of genetic risk prediction studies: the GRIPS statement.

A Cecile Jw Janssens1, John Pa Ioannidis, Cornelia M van Duijn, Julian Little, Muin J Khoury.   

Abstract

The rapid and continuing progress in gene discovery for complex diseases is fueling interest in the potential application of genetic risk models for clinical and public health practice. The number of studies assessing the predictive ability is steadily increasing, but the quality and completeness of reporting varies. A multidisciplinary workshop sponsored by the Human Genome Epidemiology Network developed a checklist of 25 items recommended for strengthening the reporting of genetic risk prediction studies (the GRIPS statement), building on the principles established by prior reporting guidelines. These recommendations aim to enhance the transparency of study reporting, and thereby to improve the synthesis and application of information from multiple studies that might differ in design, conduct, or analysis. A detailed Explanation and Elaboration document is published at http://www.plosmedicine.org.

Entities:  

Year:  2011        PMID: 21410995      PMCID: PMC3092101          DOI: 10.1186/gm230

Source DB:  PubMed          Journal:  Genome Med        ISSN: 1756-994X            Impact factor:   11.117


  26 in total

1.  The STARD statement for reporting studies of diagnostic accuracy: explanation and elaboration.

Authors:  Patrick M Bossuyt; Johannes B Reitsma; David E Bruns; Constantine A Gatsonis; Paul P Glasziou; Les M Irwig; David Moher; Drummond Rennie; Henrica C W de Vet; Jeroen G Lijmer
Journal:  Ann Intern Med       Date:  2003-01-07       Impact factor: 25.391

Review 2.  Almost all articles on cancer prognostic markers report statistically significant results.

Authors:  Panayiotis A Kyzas; Despina Denaxa-Kyza; John P A Ioannidis
Journal:  Eur J Cancer       Date:  2007-11-05       Impact factor: 9.162

Review 3.  A catalogue of reporting guidelines for health research.

Authors:  I Simera; D Moher; J Hoey; K F Schulz; D G Altman
Journal:  Eur J Clin Invest       Date:  2010-01       Impact factor: 4.686

Review 4.  Assessment of claims of improved prediction beyond the Framingham risk score.

Authors:  Ioanna Tzoulaki; George Liberopoulos; John P A Ioannidis
Journal:  JAMA       Date:  2009-12-02       Impact factor: 56.272

5.  The Human Genome Epidemiology Network.

Authors:  M J Khoury; J S Dorman
Journal:  Am J Epidemiol       Date:  1998-07-01       Impact factor: 4.897

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.  Selective reporting biases in cancer prognostic factor studies.

Authors:  Panayiotis A Kyzas; Konstantinos T Loizou; John P A Ioannidis
Journal:  J Natl Cancer Inst       Date:  2005-07-20       Impact factor: 13.506

Review 8.  Towards complete and accurate reporting of studies of diagnostic accuracy: the STARD initiative.

Authors:  Patrick M Bossuyt; Johannes B Reitsma; David E Bruns; Constantine A Gatsonis; Paul P Glasziou; Les M Irwig; Jeroen G Lijmer; David Moher; Drummond Rennie; Henrica C W de Vet
Journal:  BMJ       Date:  2003-01-04

9.  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

Review 10.  Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration.

Authors:  Jan P Vandenbroucke; Erik von Elm; Douglas G Altman; Peter C Gøtzsche; Cynthia D Mulrow; Stuart J Pocock; Charles Poole; James J Schlesselman; Matthias Egger
Journal:  PLoS Med       Date:  2007-10-16       Impact factor: 11.069

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  5 in total

Review 1.  Genetics of coronary artery disease and myocardial infarction.

Authors:  Xuming Dai; Szymon Wiernek; James P Evans; Marschall S Runge
Journal:  World J Cardiol       Date:  2016-01-26

2.  Machine learning for genetic prediction of psychiatric disorders: a systematic review.

Authors:  Matthew Bracher-Smith; Karen Crawford; Valentina Escott-Price
Journal:  Mol Psychiatry       Date:  2020-06-26       Impact factor: 15.992

3.  Atherosclerotic and thrombotic genetic and environmental determinants in Egyptian coronary artery disease patients: a pilot study.

Authors:  Manal S Fawzy; Eman A Toraih; Nagwa M Aly; Abeer Fakhr-Eldeen; Dahlia I Badran; Mohammad H Hussein
Journal:  BMC Cardiovasc Disord       Date:  2017-01-13       Impact factor: 2.298

Review 4.  Prognostic Modelling Studies of Coronary Heart Disease-A Systematic Review of Conventional and Genetic Risk Factor Studies.

Authors:  Nayla Nasr; Beáta Soltész; János Sándor; Róza Adány; Szilvia Fiatal
Journal:  J Cardiovasc Dev Dis       Date:  2022-09-05

5.  Genomic Prediction of 16 Complex Disease Risks Including Heart Attack, Diabetes, Breast and Prostate Cancer.

Authors:  Louis Lello; Timothy G Raben; Soke Yuen Yong; Laurent C A M Tellier; Stephen D H Hsu
Journal:  Sci Rep       Date:  2019-10-25       Impact factor: 4.379

  5 in total

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