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Ann Intern Med. 2019 Jan 1;170(1):51-58. doi: 10.7326/M18-1376.

PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies.

Author information

Kleijnen Systematic Reviews, York, United Kingdom (R.F.W., M.W.).
Julius Center for Health Sciences and Primary Care and Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands (K.G.M., J.B.R.).
Centre for Prognosis Research, Research Institute for Primary Care and Health Sciences, Keele University, Keele, United Kingdom (R.D.R.).
Medical School of the University of Bristol and National Institute for Health Research Collaboration for Leadership in Applied Health Research and Care West, University Hospitals Bristol National Health Service Foundation Trust, Bristol, United Kingdom (P.F.W.).
Centre for Statistics in Medicine, University of Oxford, Oxford, United Kingdom (G.S.C.).
Kleijnen Systematic Reviews, York, United Kingdom, and School for Public Health and Primary Care, Maastricht University, Maastricht, the Netherlands (J.K.).
Institute of Applied Health Research, National Institute for Health Research Birmingham Biomedical Research Centre, College of Medical and Dental Sciences, University of Birmingham, Birmingham, United Kingdom (S.M.).


Clinical prediction models combine multiple predictors to estimate risk for the presence of a particular condition (diagnostic models) or the occurrence of a certain event in the future (prognostic models). PROBAST (Prediction model Risk Of Bias ASsessment Tool), a tool for assessing the risk of bias (ROB) and applicability of diagnostic and prognostic prediction model studies, was developed by a steering group that considered existing ROB tools and reporting guidelines. The tool was informed by a Delphi procedure involving 38 experts and was refined through piloting. PROBAST is organized into the following 4 domains: participants, predictors, outcome, and analysis. These domains contain a total of 20 signaling questions to facilitate structured judgment of ROB, which was defined to occur when shortcomings in study design, conduct, or analysis lead to systematically distorted estimates of model predictive performance. PROBAST enables a focused and transparent approach to assessing the ROB and applicability of studies that develop, validate, or update prediction models for individualized predictions. Although PROBAST was designed for systematic reviews, it can be used more generally in critical appraisal of prediction model studies. Potential users include organizations supporting decision making, researchers and clinicians who are interested in evidence-based medicine or involved in guideline development, journal editors, and manuscript reviewers.

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