Wolff, RF, Moons, KGM, Riley, RD ORCID: https://orcid.org/0000-0001-8699-0735, Whiting, PF, Westwood, M, Collins, GS, Reitsma, JB, Kleijnen, J, Mallett, S and PROBAST Group†, . (2019) PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies. Annals of Internal Medicine, 170 (1). 51 - 58.

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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.

Item Type: Article
Additional Information: The final published version of this accepted manuscript can be found online at https://annals.org/aim/fullarticle/2719961/probast-tool-assess-risk-bias-applicability-prediction-model-studies
Uncontrolled Keywords: prediction model studies; bias; risk
Subjects: R Medicine > R Medicine (General)
R Medicine > RA Public aspects of medicine
Divisions: Faculty of Medicine and Health Sciences > Primary Care Health Sciences
Related URLs:
Depositing User: Symplectic
Date Deposited: 19 Feb 2019 08:20
Last Modified: 19 Feb 2019 08:20
URI: http://eprints.keele.ac.uk/id/eprint/5884

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