PROBAST: a tool for assessing risk of bias in the study of diagnostic or prognostic multi-factorial predictive models_Chinese Journal of Evidence-Based Medicine

Authors：

CHEN Xiangping ¹ , ZHANG Yi ¹ ,  ZHUANG Yiyu ² , ZHANG Zhongheng ³

1. Department of Intensive Care Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, P.R.China;
2. Department of Nursing, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, P.R.China;
3. Department of Emergency Medicine, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, P.R.China;

Corresponding author：

ZHUANG Yiyu, Email: zhuangyy@zju.edu.cn

Keywords：

PROBAST; Predictive model; Systematic review; Risk of bias

DOI：

10.7507/1672-2531.201910087

Video：

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This study aims to introduce how to use the PROBAST (prediction model risk of bias assessment tool) to evaluate risk of bias and applicability of the study of diagnostic or prognostic predictive models, including the introduction of the background, the scope of application and use of the tool. This tool mainly involves the four areas of participants, predictors, outcomes and analyses. The risk of bias in the research is evaluated through the four areas, while the applicability is evaluated in the first three. PROBAST provides a standardized approach to evaluate the critical appraisal of the study of diagnostic or prognostic predictive models, which screens qualified literature for data analysis and helps to establish a scientific basis for clinical decision-making.

Citation： CHEN Xiangping, ZHANG Yi, ZHUANG Yiyu, ZHANG Zhongheng. PROBAST: a tool for assessing risk of bias in the study of diagnostic or prognostic multi-factorial predictive models. Chinese Journal of Evidence-Based Medicine, 2020, 20(6): 737-744. doi: 10.7507/1672-2531.201910087 Copy

1.	张蕊, 郑黎强, 潘国伟. 疾病发病风险预测模型的应用与建立. 中国卫生统计, 2015, 32(4): 724-726.
2.	Grant SW, Collins GS, Nashef SAM. Statistical Primer: developing and validating a risk prediction model. Eur J Cardiothorac Surg, 2018, 54(2): 203-208.
3.	Moons KG, de Groot JA, Bouwmeester W, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS Med, 2014, 11(10), e1001744.
4.	Haider S, Sadiq SN, Moore D, et al. Prognostic prediction models for diabetic retinopathy progression: a systematic review. Eye (Lond), 2019, 33(5): 702-713.
5.	陈金宏, 何耀, 秦银河. 心血管病预测模型研究进展. 中华流行病学杂志, 2010, 31(11): 1311-1314.
6.	张佳妮, 于诗嘉, 史铁梅. 风险预测模型诊断子宫内膜癌的研究进展. 中国临床医学影像杂志, 2009, 20(2): 115-116.
7.	廖文圣, 邵彦翔, 孙光曦, 等. 预后预测模型在临床非转移肾细胞癌中的应用价值. 四川大学学报(医学版), 2019, 50(5): 720-724.
8.	徐枭喻, 王梅新, 汪惠才. 糖尿病前期人群筛查方法与风险预测模型的研究进展. 护理研究, 2017, 31(35): 4468-4471.
9.	Rabar S, Lau R, O'Flynn N, et al. Risk assessment of fragility fractures: summary of NICE guidance. BMJ, 2012, 345: e3698.
10.	Jr GD, Lloydjones DM, Bennett G, et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol, 2014, 63(25): 2935-2959.
11.	Grady D, Berkowitz SA. Why is a good clinical prediction rule so hard to find? Arch Intern Med, 2011, 171(19): 1701-1702.
12.	Meier K, Parrish J, D'Souza R. Prediction models for determining the success of labor induction: a systematic review. Acta Obstet Gynecol Scand, 2019, 98(9): 1100-1112.
13.	Debray TP, Damen JA, Snell KI, et al. A guide to systematic review and meta-analysis of prediction model performance. BMJ, 2017, 356: i6460.
14.	Collins GS, Reitsma JB, Altman DG, et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD Statement. Br J Surg, 2015, 102(3): 148-158.
15.	陶立元, 刘珏, 曾琳, 等. 针对个体的预后或诊断多因素预测模型报告规范(TRIPOD)解读. 中华医学杂志, 2018, 98(44): 3556-3560.
16.	郑黎强, 张蕊. 疾病发病风险预测模型拟合度评价方法的研究进展. 中国卫生统计, 2015, 32(3): 544-546.
17.	Wolff RF, Moons KGM, Riley RD, et al. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med, 2019, 170(1): 51-58.
18.	王俊峰, 章仲恒, 周支瑞, 等. 临床预测模型: 模型的验证. 中国循证心血管医学杂志, 2019, 11(2): 141-144.
19.	安莹, 王艳玲. 慢性阻塞性肺疾病急性加重期患者短期预后预测模型的建立. 中华护理杂志, 2019, 54(1): 42-46.
20.	Moons KGM, Wolff RF, Riley RD, et al. PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration. Ann Intern Med, 2019, 170(1): W1-W33.

1. 张蕊, 郑黎强, 潘国伟. 疾病发病风险预测模型的应用与建立. 中国卫生统计, 2015, 32(4): 724-726.
2. Grant SW, Collins GS, Nashef SAM. Statistical Primer: developing and validating a risk prediction model. Eur J Cardiothorac Surg, 2018, 54(2): 203-208.
3. Moons KG, de Groot JA, Bouwmeester W, et al. Critical appraisal and data extraction for systematic reviews of prediction modelling studies: the CHARMS checklist. PLoS Med, 2014, 11(10), e1001744.
4. Haider S, Sadiq SN, Moore D, et al. Prognostic prediction models for diabetic retinopathy progression: a systematic review. Eye (Lond), 2019, 33(5): 702-713.
5. 陈金宏, 何耀, 秦银河. 心血管病预测模型研究进展. 中华流行病学杂志, 2010, 31(11): 1311-1314.
6. 张佳妮, 于诗嘉, 史铁梅. 风险预测模型诊断子宫内膜癌的研究进展. 中国临床医学影像杂志, 2009, 20(2): 115-116.
7. 廖文圣, 邵彦翔, 孙光曦, 等. 预后预测模型在临床非转移肾细胞癌中的应用价值. 四川大学学报(医学版), 2019, 50(5): 720-724.
8. 徐枭喻, 王梅新, 汪惠才. 糖尿病前期人群筛查方法与风险预测模型的研究进展. 护理研究, 2017, 31(35): 4468-4471.
9. Rabar S, Lau R, O'Flynn N, et al. Risk assessment of fragility fractures: summary of NICE guidance. BMJ, 2012, 345: e3698.
10. Jr GD, Lloydjones DM, Bennett G, et al. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines. J Am Coll Cardiol, 2014, 63(25): 2935-2959.
11. Grady D, Berkowitz SA. Why is a good clinical prediction rule so hard to find? Arch Intern Med, 2011, 171(19): 1701-1702.
12. Meier K, Parrish J, D'Souza R. Prediction models for determining the success of labor induction: a systematic review. Acta Obstet Gynecol Scand, 2019, 98(9): 1100-1112.
13. Debray TP, Damen JA, Snell KI, et al. A guide to systematic review and meta-analysis of prediction model performance. BMJ, 2017, 356: i6460.
14. Collins GS, Reitsma JB, Altman DG, et al. Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD): the TRIPOD Statement. Br J Surg, 2015, 102(3): 148-158.
15. 陶立元, 刘珏, 曾琳, 等. 针对个体的预后或诊断多因素预测模型报告规范(TRIPOD)解读. 中华医学杂志, 2018, 98(44): 3556-3560.
16. 郑黎强, 张蕊. 疾病发病风险预测模型拟合度评价方法的研究进展. 中国卫生统计, 2015, 32(3): 544-546.
17. Wolff RF, Moons KGM, Riley RD, et al. PROBAST: a tool to assess the risk of bias and applicability of prediction model studies. Ann Intern Med, 2019, 170(1): 51-58.
18. 王俊峰, 章仲恒, 周支瑞, 等. 临床预测模型: 模型的验证. 中国循证心血管医学杂志, 2019, 11(2): 141-144.
19. 安莹, 王艳玲. 慢性阻塞性肺疾病急性加重期患者短期预后预测模型的建立. 中华护理杂志, 2019, 54(1): 42-46.
20. Moons KGM, Wolff RF, Riley RD, et al. PROBAST: a tool to assess risk of bias and applicability of prediction model studies: explanation and elaboration. Ann Intern Med, 2019, 170(1): W1-W33.

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