An interpretation of QUAPAS: a tool for assessing risk of bias in prognostic accuracy studies_Chinese Journal of Evidence-Based Medicine

Authors：

ZHANG Mei ¹ , WEI Jianhua ² ,  SHEN Jiantong ¹ , HUA Yuting ¹ , HUANG Yuxiang ¹ , LIU Yujin ¹

1. Department of Huzhou University, School of Nursing Science, Huzhou 313000, P. R. China;
2. The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310000, P. R. China;

Corresponding author：

SHEN Jiantong, Email: 02198@zjhu.edu.cn

Keywords：

QUAPAS; QUADAS-2; Prognostic accuracy studies; Systematic review; Risk of bias assessment

DOI：

10.7507/1672-2531.202211107

Video：

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The QUADAS-2, QUIPS, and PROBAST tools are not specific for prognostic accuracy studies and the use of these tools to assess the risk of bias in prognostic accuracy studies is prone to bias. Therefore, QUAPAS, a risk of bias assessment tool for prognostic accuracy studies, has recently been developed. The tool combines QUADAS-2, QUIPS, and PROBAST, and consists of 5 domains, 18 signaling questions, 5 risk of bias questions, and 4 applicability questions. This paper will introduce the content and usage of QUAPAS to provide inspiration and references for domestic researchers.

Citation： ZHANG Mei, WEI Jianhua, SHEN Jiantong, HUA Yuting, HUANG Yuxiang, LIU Yujin. An interpretation of QUAPAS: a tool for assessing risk of bias in prognostic accuracy studies. Chinese Journal of Evidence-Based Medicine, 2023, 23(4): 457-464. doi: 10.7507/1672-2531.202211107 Copy

1.	Moons KG, Royston P, Vergouwe Y, et al. Prognosis and prognostic research: what, why, and how. BMJ, 2009, 338: b375.
2.	唐少文, 张渊, 陶必林, 等. 偏倚风险评估系列: (七)预后因素研究. 中华流行病学杂志, 2018, 39(7): 1003-1008.
3.	陈香萍, 张奕, 庄一渝, 等. PROBAST: 诊断或预后多因素预测模型研究偏倚风险的评估工具. 中国循证医学杂志, 2020, 20(6): 737-744.
4.	Deawjaroen K, Sillabutra J, Poolsup N, et al. Clinical usefulness of prediction tools to identify adult hospitalized patients at risk of drug-related problems: a systematic review of clinical prediction models and risk assessment tools. Br J Clin Pharmacol, 2022, 88(4): 1613-1629.
5.	Reynolds JC, Issa MS, C Nicholson T, et al. Prognostication with point-of-care echocardiography during cardiac arrest: a systematic review. Resuscitation, 2020, 152: 56-68.
6.	Lee J, Mulder F, Leeflang M, et al. QUAPAS: an adaptation of the QUADAS-2 tool to assess prognostic accuracy studies. Ann Intern Med, 2022, 175(7): 1010-1018.
7.	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.
8.	邓建新, 单路宝, 贺德强, 等. 缺失数据的处理方法及其发展趋势. 统计与决策, 2019, 35(23): 28-34.
9.	Donoghoe MW, Gebski V. The importance of censoring in competing risks analysis of the subdistribution hazard. BMC Med Res Methodol, 2017, 17(1): 52.
10.	Staffa SJ, Zurakowski D. Competing risks analysis of time-to-event data for cardiovascular surgeons. J Thorac Cardiovasc Surg, 2020, 159(6): 2459-2466.
11.	Valenta Z, Skrabaka D, Owczarek AJ, et al. Kidney graft failure and patient survival modelling based on competing risks under nonproportional hazards. Transplant Proc, 2022, 54(4): 940-947.
12.	Li C, Li J, Huang Q, et al. Developing and validating a novel nomogram used a competing-risks model for predicting the prognosis of primary fallopian tube carcinoma: a retrospective study based on the SEER database. Ann Transl Med, 2021, 9(5): 378.
13.	Hagström H, Nasr P, Ekstedt M, et al. Accuracy of noninvasive scoring systems in assessing risk of death and liver-related endpoints in patients with nonalcoholic fatty liver disease. Clin Gastroenterol Hepatol, 2019, 17(6): 1148-1156.
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. BMJ, 2015, 350: g7594.
15.	Sauerbrei W, Taube SE, McShane LM, et al. Reporting recommendations for tumor marker prognostic studies (REMARK): an abridged explanation and elaboration. J Natl Cancer Inst, 2018, 110(8): 803-811.

1. Moons KG, Royston P, Vergouwe Y, et al. Prognosis and prognostic research: what, why, and how. BMJ, 2009, 338: b375.
2. 唐少文, 张渊, 陶必林, 等. 偏倚风险评估系列: (七)预后因素研究. 中华流行病学杂志, 2018, 39(7): 1003-1008.
3. 陈香萍, 张奕, 庄一渝, 等. PROBAST: 诊断或预后多因素预测模型研究偏倚风险的评估工具. 中国循证医学杂志, 2020, 20(6): 737-744.
4. Deawjaroen K, Sillabutra J, Poolsup N, et al. Clinical usefulness of prediction tools to identify adult hospitalized patients at risk of drug-related problems: a systematic review of clinical prediction models and risk assessment tools. Br J Clin Pharmacol, 2022, 88(4): 1613-1629.
5. Reynolds JC, Issa MS, C Nicholson T, et al. Prognostication with point-of-care echocardiography during cardiac arrest: a systematic review. Resuscitation, 2020, 152: 56-68.
6. Lee J, Mulder F, Leeflang M, et al. QUAPAS: an adaptation of the QUADAS-2 tool to assess prognostic accuracy studies. Ann Intern Med, 2022, 175(7): 1010-1018.
7. 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.
8. 邓建新, 单路宝, 贺德强, 等. 缺失数据的处理方法及其发展趋势. 统计与决策, 2019, 35(23): 28-34.
9. Donoghoe MW, Gebski V. The importance of censoring in competing risks analysis of the subdistribution hazard. BMC Med Res Methodol, 2017, 17(1): 52.
10. Staffa SJ, Zurakowski D. Competing risks analysis of time-to-event data for cardiovascular surgeons. J Thorac Cardiovasc Surg, 2020, 159(6): 2459-2466.
11. Valenta Z, Skrabaka D, Owczarek AJ, et al. Kidney graft failure and patient survival modelling based on competing risks under nonproportional hazards. Transplant Proc, 2022, 54(4): 940-947.
12. Li C, Li J, Huang Q, et al. Developing and validating a novel nomogram used a competing-risks model for predicting the prognosis of primary fallopian tube carcinoma: a retrospective study based on the SEER database. Ann Transl Med, 2021, 9(5): 378.
13. Hagström H, Nasr P, Ekstedt M, et al. Accuracy of noninvasive scoring systems in assessing risk of death and liver-related endpoints in patients with nonalcoholic fatty liver disease. Clin Gastroenterol Hepatol, 2019, 17(6): 1148-1156.
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. BMJ, 2015, 350: g7594.
15. Sauerbrei W, Taube SE, McShane LM, et al. Reporting recommendations for tumor marker prognostic studies (REMARK): an abridged explanation and elaboration. J Natl Cancer Inst, 2018, 110(8): 803-811.

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An interpretation of QUAPAS: a tool for assessing risk of bias in prognostic accuracy studies

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