Scientific reporting is suboptimal for aspects that characterize genetic risk prediction studies: a review of published articles based on the Genetic RIsk Prediction Studies statement

AI Iglesias, Raluca Mihaescu, JPA Ioannidis, MJ Khoury, J Little, Cornelia Duijn, Cecile Janssens

Research output: Contribution to journalArticleAcademicpeer-review

6 Citations (Scopus)


Objectives: Our main objective was to raise awareness of the areas that need improvements in the reporting of genetic risk prediction articles for future publications, based on the Genetic Risk Prediction Studies (GRIPS) statement. Study Design and Setting: We evaluated studies that developed or validated a prediction model based on multiple DNA variants, using empirical data, and were published in 2010. A data extraction form based on the 25 items of the GRIPS statement was created and piloted. Results: Forty-two studies met our inclusion criteria. Overall, more than half of the evaluated items (34 of 62) were reported in at least 85% of included articles. Seventy-seven percentage of the articles were identified as genetic risk prediction studies through title assessment, but only 31% used the keywords recommended by GRIPS in the title or abstract. Seventy-four percentage mentioned which allele was the risk variant. Overall, only 10% of the articles reported all essential items needed to perform external validation of the risk model. Conclusion: Completeness of reporting in genetic risk prediction studies is adequate for general elements of study design but is suboptimal for several aspects that characterize genetic risk prediction studies such as description of the model construction. Improvements in the transparency of reporting of these aspects would facilitate the identification, replication, and application of genetic risk prediction models. (C) 2014 Elsevier Inc. All rights reserved.
Original languageUndefined/Unknown
Pages (from-to)487-499
Number of pages13
JournalJournal of Clinical Epidemiology
Issue number5
Publication statusPublished - 2014

Research programs

  • EMC NIHES-01-64-03

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