PROBAST: A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies: Explanation and Elaboration
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Résumé
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.
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La notice
- Revue
- Annals of Internal Medicine
- Thématique
- Health Systems, Economic Evaluations, Quality of Life
- Domaine
- Economics, Econometrics and Finance
- Établissements canadiens
- —
- Organismes subventionnaires
- Birmingham Biomedical Research CentreNIHR Oxford Biomedical Research CentreCare and Public Health Research Institute, Universiteit MaastrichtErasmus Universitair Medisch Centrum RotterdamRadboud Universitair Medisch CentrumKeele UniversityLeids Universitair Medisch CentrumNederlandse Organisatie voor Wetenschappelijk OnderzoekUniversity of OxfordVrije Universiteit AmsterdamNIHR School for Primary Care ResearchRadboud UniversiteitUniversiteit van AmsterdamUniversiteit LeidenUniversity of BristolUniversiteit MaastrichtUniversitair Medisch Centrum UtrechtZonMwUniversity of ExeterNational Institute for Health and Care ResearchAlbert-Ludwigs-Universität FreiburgRoyal College of Surgeons in IrelandNational Institute for Health and Care ExcellenceUniversity Hospitals Birmingham NHS Foundation TrustVanderbilt UniversityUniversity Hospitals Bristol NHS Foundation TrustMcMaster UniversityDalhousie UniversityCancer Research UKLondon School of Hygiene and Tropical MedicineUniversiteit UtrechtUniversity of BernMemorial Sloan-Kettering Cancer Center
- Mots-clés
- ChecklistGuidelineMedicinePopulationSystematic reviewOutcome (game theory)Process (computing)Predictive modellingHealth careMEDLINERisk analysis (engineering)Management scienceComputer scienceArtificial intelligenceMachine learningPsychologyCognitive psychologyPathologyEngineering
- Résumé présent dans OpenAlex
- oui