陈茹,王胜锋,周家琛,孙凤,魏文强,詹思延.预测模型研究的偏倚风险和适用性评估工具解读[J].中华流行病学杂志,2020,41(5):776-781 |
预测模型研究的偏倚风险和适用性评估工具解读 |
Introduction of the Prediction model Risk Of Bias ASsessment Tool: a tool to assess risk of bias and applicability of prediction model studies |
收稿日期:2019-08-05 出版日期:2020-05-12 |
DOI:10.3760/cma.j.cn112338-20190805-00580 |
中文关键词: 偏倚风险 评估工具 预测模型研究 系统综述 |
英文关键词: Risk of bias Tool for assessment Prediction model studies Systematic review |
基金项目:国家重点研发计划(2016YFC0901404,2018YFC1311704);国家自然科学基金(81903403);北京市自然科学基金(7204294);协和青年科研基金(2017330004) |
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中文摘要: |
本文介绍了预测模型研究的偏倚风险和适用性评估工具PROBAST(Prediction model Risk Of Bias ASsessment Tool)的主要内容、评价步骤和相关注意事项。PROBAST从研究对象、预测因素、结局和分析4个领域共20个信号问题对原始研究的设计、实施和分析过程中可能产生的偏倚风险和适用性进行评价。通过综合分析,对原始研究每个领域和整体的偏倚风险和适用性做出判断,分为高、低或不清楚。PROBAST为个体预测模型开发、验证和更新提供了可靠的新评价工具,它不仅可以用于预测模型的系统综述,也可作为预测模型研究通用的方法学评价工具。 |
英文摘要: |
This paper introduceds the tool named as "Prediction model Risk Of Bias ASsessment Tool" (PROBAST) to assess the risk of bias and applicability in prediction model studies and the relevant items and steps of assessment. PROBAST is organized into four domains including participants, predictors, outcome and analysis. These domains contain a total of 20 signaling questions to facilitate structured judgment of risk of bias occurring in study design, conduct or analysis. Through comprehensive judgment, the risk of bias and applicability of original study is categorized as high, low or unclear. PROBAST enables a focused and transparent approach to assessing the risk of bias of studies that develop, validate, or update prediction models for individualized predictions. Although PROBAST was designed for systematic reviews, it can be also used more generally in critical appraisal of prediction model studies. |
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