PROBAST: A tool to assess the risk of bias and applicability of prediction model studies Journal Article


Authors: Wolff, R. F.; Moons, K. G. M.; Riley, R. D.; Whiting, P. F.; Westwood, M.; Collins, G. S.; Reitsma, J. B.; Kleijnen, J.; Mallett, S.; for the PROBAST Group
Contributor: Vickers, A.
Article Title: PROBAST: A tool to assess the risk of bias and applicability of prediction model studies
Abstract: 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.
Keywords: diagnosis tripod; individual prognosis
Journal Title: Annals of Internal Medicine
Volume: 170
Issue: 1
ISSN: 0003-4819
Publisher: American College of Physicians  
Date Published: 2019-01-01
Start Page: 51
End Page: 58
Language: English
ACCESSION: WOS:000454685300011
DOI: 10.7326/m18-1376
PROVIDER: wos
PUBMED: 30596875
Notes: Article -- Source: Wos
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  1. Andrew J Vickers
    544 Vickers