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

Robert F. Wolff*, Karel G. M. Moons, Richard D. Riley, Penny F. Whiting, Marie Westwood, Gary S. Collins, Johannes B. Reitsma, Jos Kleijnen, Sue Mallett, Doug Altman, Patrick Bossuyt, Nancy R. Cook, Gennaro D'Amico, Thomas P. A. Debray, Jon Deeks, Joris de Groot, Emanuele di Angelantonio, Tom Fahey, Frank Harrell, Jill A. HaydenMartijn W. Heymans, Lotty Hooft, Chris Hyde, John Ioannidis, Alfonso Iorio, Stephen Kaptoge, Andre Knottnerus, Mariska Leeflang, Frances Nixon, Pablo Perel, Bob Phillips, Heike Raatz, Rob Riemsma, Maroeska Rovers, Anne W. S. Rutjes, Willi Sauerbrei, Stefan Sauerland, Fueloep Scheibler, Rob Scholten, Ewoud Schuit, Ewout Steyerberg, Toni Tan, Gerben ter Riet, Danielle van der Windt, Yvonne Vergouwe, Andrew Vickers, Angela M. Wood, PROBAST group, PROBAST Steering Group, PROBAST Delphi Group

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review


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.

Original languageEnglish
Pages (from-to)51-60
Number of pages10
JournalAnnals of Internal Medicine
Issue number1
Publication statusPublished - 1 Jan 2019


  • Explanation
  • Individual prognosis
  • Diagnosis tripod


Dive into the research topics of 'PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies'. Together they form a unique fingerprint.

Cite this