PROBAST: A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies: Explanation and Elaboration
PROBAST: A Tool to Assess Risk of Bias and Applicability of Prediction Model Studies: Explanation and Elaboration
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DOI：
10.7326/m18-1377
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发表时间：
2019-01-01
影响因子：
39.2
通讯作者：
Mallett, Sue
中科院分区：
文献类型：
--
作者：
Moons, Karel G. M.;Wolff, Robert F.;Mallett, Sue
医疗保健中的预测模型使用预测因子来估计个体已经存在（诊断模型）或将来发生（预后模型）的状况或疾病的概率。近年来，关于预测模型的出版物变得越来越普遍，并且经常存在竞争预测模型用于相同的结果或目标人群。卫生保健提供者、指南制定者和政策制定者通常不确定使用或推荐哪种模型，以及在哪些人或环境中使用。因此，对这些研究进行系统评价的需求、要求和实施日益增加。预测模型系统评价的一个关键部分是检查偏倚风险和对预期人群和环境的适用性。为了帮助评审人员完成这一过程，作者开发了PROBAST（预测模型偏倚风险评估工具），用于开发、验证或更新（例如，扩展）诊断和预后预测模型的研究。PROBAST是通过该领域专家组的共识过程开发的。它包括4个领域（参与者，预测因素，结果和分析）的20个信号问题。本解释和阐述文件描述了纳入每个领域和信号问题的基本原理，并指导研究人员、评审人员、读者和指南制定者如何使用它们来评估偏倚风险和适用性问题。所有的概念都说明了不同主题的出版的例子。最新版本的PROBAST检查表、随附文档和填写的示例可从www.probast.org下载。
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.