| 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].Chinese journal of Epidemiology,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 |
| Received:August 05, 2019 |
| DOI：10.3760/cma.j.cn112338-20190805-00580 |
| KeyWord: 偏倚风险 评估工具 预测模型研究 系统综述 |
| English Key Word: Risk of bias Tool for assessment Prediction model studies Systematic review |
| FundProject: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） |
| Author Name | Affiliation | E-mail | | Chen Ru | Office for Cancer Registry, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China | | | Wang Shengfeng | Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China | | | Zhou Jiachen | Department of Epidemiology and Biostatistics, School of Public Health, Xi'an Jiaotong University Health Science Center, Xi'an 710061, China | | | Sun Feng | Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China | | | Wei Wenqiang | Office for Cancer Registry, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100021, China | | | Zhan Siyan | Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing 100191, China | siyan-zhan@bjmu.edu.cn |
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| Abstract: |
| 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。 |
| English Abstract: |
| 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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