PROBAST: A tool to assess risk of bias and applicability of prediction model studies: Explanation and elaboration

Karel G.M. Moons*, Robert F. Wolff, Richard D. Riley, Penny F. Whiting, Marie Westwood, Gary S. Collins, Johannes B. Reitsma, Jos Kleijnen, Sue Mallett

*Corresponding author for this work

Research output: Contribution to journalReview article (Academic Journal)peer-review

102 Citations (Scopus)
292 Downloads (Pure)

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.

Original languageEnglish
Pages (from-to)W1-W33
Number of pages33
JournalAnnals of Internal Medicine
Volume170
Issue number1
DOIs
Publication statusPublished - 1 Jan 2019

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