Strengthening the reporting of Genetic RIsk Prediction Studies (GRIPS): explanation and elaboration

Janssens, A. Cecile J.W.; Ioannidis, John P. A.; Bedrosian, Sara; Boffetta, Paolo; Dolan, Siobhan M.; Dowling, Nicole F.; Fortier, Isabel; Freedman, Andrew; Grimshaw, Jeremy; Gulcher, Jeffrey R.; Gwinn, Marta; Hlatky, Mark A.; Janes, Holly; Kraft, Peter; Melillo, Stephanie; O’Donnell, Christopher J.; Pencina, Michael J.; Ransohoff, David F.; Schully, Sheri D.; Seminara, Daniela; Winn, Deborah M.; Wright, Caroline F.; Duijn, Cornelia M. van; Little, Julian; Khoury, Muin J.

doi:10.1016/j.jclinepi.2011.02.003

Cited by 18 publications

(13 citation statements)

References 97 publications

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“…[47][48][49] Each checklist item is explained and accompanied by examples of good reporting from published articles. In addition, because many such studies are methodologically weak, we also summarise the qualities of good (and the limitations of less good) studies, regardless of reporting.…”

Section: The Tripod Explanation and Elaboration Documentmentioning

confidence: 99%

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement

Collins

Reitsma

Altman

et al. 2015

BMJ

2,704

1,845

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Prediction models are developed to aid health care providers in estimating the probability or risk that a specific disease or condition is present (diagnostic models) or that a specific event will occur in the future (prognostic models), to inform their decision making. However, the overwhelming evidence shows that the quality of reporting of prediction model studies is poor. Only with full and clear reporting of information on all aspects of a prediction model can risk of bias and potential usefulness of prediction models be adequately assessed. The Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Initiative developed a set of recommendations for the reporting of studies developing, validating, or updating a prediction model, whether for diagnostic or prognostic purposes. This article describes how the TRIPOD Statement was developed. An extensive list of items based on a review of the literature was created, which was reduced after a Web based survey and revised during a three day meeting in June 2011 with methodologists, health care professionals, and journal editors. The list was refined during several meetings of the steering group and in e-mail discussions with the wider group of TRIPOD contributors. The resulting TRIPOD Statement is a checklist of 22 items, deemed essential for transparent reporting of a prediction model study. The TRIPOD Statement aims to improve the transparency of the reporting of a prediction model study regardless of the study methods used. The TRIPOD Statement is best used in conjunction with the TRIPOD explanation and elaboration document. To aid the editorial process and readers of prediction model studies, it is recommended that authors include a completed checklist in their submission (also available at www.tripod-statement.org).

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Section: The Tripod Explanation and Elaboration Documentmentioning

confidence: 99%

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement

Collins

Reitsma

Altman

et al. 2015

BMJ

2,704

1,845

View full text Add to dashboard Cite

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“…Various reviews have discussed the poor reporting and conduct in the field of clinical prediction modelling 30–35. This has also led to the recent Genetic Risk Prediction Studies (GRIPS) statement to strengthen the reporting and, indirectly, conduct of risk prediction studies with genetic predictors 36 37. Moreover, the number of published prediction models, even for the same disorder or clinical domain, has sharply increased in the last decade 2.…”

Section: Introductionmentioning

confidence: 99%

Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker

Moons

Kengne

Woodward

et al. 2012

Heart

721

756

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Prediction models are increasingly used to complement clinical reasoning and decision making in modern medicine in general, and in the cardiovascular domain in particular. Developed models first and foremost need to provide accurate and (internally and externally) validated estimates of probabilities of specific health conditions or outcomes in targeted patients. The adoption of such models must guide physician's decision making and an individual's behaviour, and consequently improve individual outcomes and the cost-effectiveness of care. In a series of two articles we review the consecutive steps generally advocated for risk prediction model research. This first article focuses on the different aspects of model development studies, from design to reporting, how to estimate a model's predictive performance and the potential optimism in these estimates using internal validation techniques, and how to quantify the added or incremental value of new predictors or biomarkers (of whatever type) to existing predictors. Each step is illustrated with empirical examples from the cardiovascular field.

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“…The checklist includes several recommendations on reporting data and on discussion and disclosure of funding. The knowledge arising from these studies will be used to guide screening strategies, as well as diagnostic and therapeutic approaches, in conjunction with existing risk prediction models [96,97].…”

Section: • • Risk Prediction Using Snp Datamentioning

confidence: 99%

Translating Genetic Risk Factors for Prostate Cancer to the Clinic: 2013 and Beyond

et al. 2014

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Prostate cancer (PrCa) is the most commonly diagnosed cancer in the male UK population, with over 40,000 new cases per year. PrCa has a complex, polygenic predisposition, due to rare variants such as BRCA and common variants such as single nucleotide polymorphisms (SNPs). With the introduction of genome-wide association studies, 78 susceptibility loci (SNPs) associated with PrCa risk have been identified. Genetic profiling could risk-stratify a population, leading to the discovery of a higher proportion of clinically significant disease and a reduction in the morbidity related to age-based prostate-specific antigen screening. Based on the combined risk of the 78 SNPs identified so far, the top 1% of the risk distribution has a 4.7-times higher risk of developing PrCa compared with the average of the general population.

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Strengthening the reporting of Genetic RIsk Prediction Studies (GRIPS): explanation and elaboration

Cited by 18 publications

References 97 publications

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement

Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD statement

Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker

Translating Genetic Risk Factors for Prostate Cancer to the Clinic: 2013 and Beyond

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