1. PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies
- Author
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Wolff, Robert F, Moons, Karel GM, Riley, Richard D, Whiting, Penny F, Westwood, Marie, Collins, Gary S, Reitsma, Johannes B, Kleijnen, Jos, Mallett, Sue, Altman, Doug, Bossuyt, Patrick, Cook, Nancy R, D'Amico, Gennaro, Debray, Thomas PA, Deeks, Jon, De Groot, Joris, Di Angelantonio, Emanuele, Fahey, Tom, Harrell, Frank, Hayden, Jill A, Heymans, Martijn W, Hooft, Lotty, Hyde, Chris, Ioannidis, John, Iorio, Alfonso, Kaptoge, Stephen, Knottnerus, Andre, Leeflang, Mariska, Nixon, Frances, Perel, Pablo, Phillips, Bob, Raatz, Heike, Riemsma, Rob, Rovers, Maroeska, Rutjes, Anne WS, Sauerbrei, Willi, Sauerland, Stefan, Scheibler, Fueloep, Scholten, Rob, Schuit, Ewoud, Steyerberg, Ewout, Tan, Toni, Ter Riet, Gerben, Van Der Windt, Danielle, Vergouwe, Yvonne, Vickers, Andrew, Wood, Angela M, Grp, PROBAST, Grp, PROBAST Steering, Grp, PROBAST Delphi, Family Medicine, RS: CAPHRI - R6 - Promoting Health & Personalised Care, RS: CAPHRI - R5 - Optimising Patient Care, Bureau FHML, Public Health, Di Angelantonio, Emanuele [0000-0001-8776-6719], Kaptoge, Stephen [0000-0002-1155-4872], Wood, Angela [0000-0002-7937-304X], and Apollo - University of Cambridge Repository
- Subjects
Delphi Technique ,Delphi method ,MEDLINE ,01 natural sciences ,Decision Support Techniques ,03 medical and health sciences ,All institutes and research themes of the Radboud University Medical Center ,0302 clinical medicine ,Bias ,Diagnosis ,Internal Medicine ,Medicine ,Humans ,030212 general & internal medicine ,0101 mathematics ,computer.programming_language ,Models, Statistical ,business.industry ,CARDIOVASCULAR RISK ,Explanation ,010102 general mathematics ,DIAGNOSIS TRIPOD ,INDIVIDUAL PROGNOSIS ,General Medicine ,Evidence-based medicine ,Prognosis ,R1 ,Data science ,Critical appraisal ,Systematic review ,Research Design ,Urological cancers Radboud Institute for Health Sciences [Radboudumc 15] ,Model risk ,business ,RA ,computer ,Predictive modelling ,Delphi ,Systematic Reviews as Topic - Abstract
Prediction models in health care use predictors to estimate for an individual the probability that a condition or disease is already present (diagnostic model) or will occur in the future (prognostic model). Publications on prediction models have become more common in recent years, and competing prediction models frequently exist for the same outcome or target population. Health care providers, guideline developers, and policymakers are often unsure which model to use or recommend, and in which persons or settings. Hence, systematic reviews of these studies are increasingly demanded, required, and performed. A key part of a systematic review of prediction models is examination of risk of bias and applicability to the intended population and setting. To help reviewers with this process, the authors developed PROBAST (Prediction model Risk Of Bias ASsessment Tool) for studies developing, validating, or updating (for example, extending) prediction models, both diagnostic and prognostic. PROBAST was developed through a consensus process involving a group of experts in the field. It includes 20 signaling questions across 4 domains (participants, predictors, outcome, and analysis). This explanation and elaboration document describes the rationale for including each domain and signaling question and guides researchers, reviewers, readers, and guideline developers in how to use them to assess risk of bias and applicability concerns. All concepts are illustrated with published examples across different topics. The latest version of the PROBAST checklist, accompanying documents, and filled-in examples can be downloaded from www.probast.org.
- Published
- 2019