Strengthening the reporting of genetic risk prediction studies (GRIPS): explanation and elaboration

Abstract

The rapid and continuing progress in gene discovery for complex diseases is fuelling 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 they vary widely in completeness of reporting and apparent quality.Transparent reporting of the strengths and weaknesses of these studies is important to facilitate the accumulation of evidence on genetic risk prediction.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 (GRIPS), building on the principles established by prior reporting guidelines.These recommendations aim to enhance the transparency, quality and completeness of study reporting, and thereby to improve the synthesis and application of information from multiple studies that might differ in design, conduct or analysis.

Improving reporting standards for polygenic scores in risk prediction studies

“…In 2011, an international working group published the Genetic Risk Prediction Studies (GRIPS) Statement, a reporting guideline for the study of risk prediction models that include genetic variants, from genetic mutations to gene scores. 57 This guideline is analogous to guidelines developed for observational epidemiological studies (STROBE 58 ) and genome-wide association studies (STREGA 59 ), and is in line with the reporting guideline for multivariate prediction models (TRIPOD 60 ). Adherence to reporting statements has been low, and the same holds for GRIPS.…”

Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies: The CHARMS Checklist

“…First, we reviewed the existing reporting guidelines for other types of clinical research including CONSORT, REMARK, STARD, STROBE, GRIPS [33] – [37] and for the reporting of systematic reviews (PRISMA) [38] . Furthermore, we considered existing quality assessment tools including the Cochrane Risk of Bias tool [39] for randomised therapeutic studies, QUADAS (and QUADAS-2) for diagnostic accuracy studies [40] , [41] , and the QUIPS checklist for appraisal of prognostic factor studies [25] , [26] .…”

“…We then reviewed published systematic reviews of prediction models and prognostic factor studies, along with the checklists or quality appraisal criteria used in those reviews [12] , [27] – [29] , [42] – [46] . Finally, we identified key methodological literature discussing recommended approaches for the design, conduct, analysis, and reporting of prediction models, followed by a search of the corresponding reference lists [3] , [19] , [31] , [32] , [37] , [47] – [59] .…”

“…All tools for reporting of medical studies recommend discussing strengths, weaknesses, and future challenges of a study and its reported results [33] – [37] , including the PRISMA statement for reporting of systematic reviews itself [38] . How a model was developed and validated and its reported performance give insight into whether the reviewed model is likely to be useful, and for whom.…”

The Software Ontology (SWO): a resource for reproducibility in biomedical data analysis, curation and digital preservation

“…We report on the Software Ontology (SWO) [ 1 , 2 ], an ontology for describing the software used within computational biology, which includes bioinformatics resources and any software tools used in the preparation and maintenance of data. Development of the SWO is motivated by the growing interest in the recording and reproducibility of biomedical investigations [ 3 , 4 ]. Reproducibility is as important for computational investigations of data as it is for investigations in the ‘wet’ laboratory [ 5 , 6 ].…”