An overview of the COSMIN-RoB checklist and the interpretation of it in evaluating the risk of bias of studies on internal structure_Chinese Journal of Evidence-Based Medicine

Authors：

PENG Jian ^1,2 , SHEN Lanjun ^1,3 , CHEN Yiting ¹ , ZHOU Tong ^1,2 , CUI Yuanbin ^1,2 , ZOU Luoluo ^1,2 ,  HU Yan ^1,2

1. School of Nursing, Fudan University, Shanghai 200032, P.R.China;
2. Evidence-Based Nursing Center, Fudan University, Shanghai 200032, P.R.China;
3. East China Hospital, Fudan University, Shanghai 200040, P.R.China;

Corresponding author：

HU Yan, Email: huyan@fudan.edu.cn

Keywords：

Patient-reported outcome measures (PROMs); Measurement properties; Risk of bias; Structural validity; Internal consistency; Cross-cultural validity; Measurement invariance; COSMIN

DOI：

10.7507/1672-2531.202003163

Video：

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Abstract Full text Figures/Tables Video References Cited by

Measurement properties studies of patient-reported outcome measures (PROMs) aims to validate the measurement properties of PROMs. In the process of designing and statistical analysis of these measurement properties studies, bias will occur if there are any defects, which will affect the quality of PROMs. Therefore, the COSMIN (consensus-based standards for the selection of health measurement instruments) team has developed the COSMIN risk of bias (COSMIN-RoB) checklist to evaluate risk of bias of studies on measurement properties of PROMs. The checklist can be used to develop systematic reviews of PROMs measurement properties, and for PROMs developers, it can also be used to guide the research design in the measurement tool development process for reducing bias. At present, similar assessment tools are lacking in China. Therefore, this article aims to introduce the primary contents of COSMIN-RoB checklist and to interpret how to evaluate risk of bias of the internal structure studies of PROMs with examples.

Citation： PENG Jian, SHEN Lanjun, CHEN Yiting, ZHOU Tong, CUI Yuanbin, ZOU Luoluo, HU Yan. An overview of the COSMIN-RoB checklist and the interpretation of it in evaluating the risk of bias of studies on internal structure. Chinese Journal of Evidence-Based Medicine, 2020, 20(10): 1234-1240. doi: 10.7507/1672-2531.202003163 Copy

1.	Prinsen CAC, Mokkink LB, Bouter LM, et al. COSMIN guideline for systematic reviews of patient-reported outcome measures. Qual Life Res, 2018, 27(5): 1147-1157.
2.	Mokkink LB, Terwee CB, Patrick DL, et al. The COSMIN checklist for assessing the methodological quality of studies on measurement properties of health status measurement instruments: an international Delphi study. Qual Life Res, 2010, 19(4): 539-549.
3.	Mokkink LB, de Vet HCW, Prinsen CAC, et al. COSMIN Risk of Bias checklist for systematic reviews of patient-reported outcome measures. Qual Life Res, 2018, 27(5): 1171-1179.
4.	程玲灵, 孙玉倩. 癌症患者自我管理测评量表的编制及信效度检验. 中华护理杂志, 2017, 52(9): 1082-1087.
5.	林少炜, 李煌元, 赵璧, 等. 亚健康量表中医生和护士的测量不变性. 卫生研究, 2018, 47(3): 399-402.
6.	Mokkink LB, Terwee CB, Gibbons E, et al. Inter-rater agreement and reliability of the COSMIN (COnsensus-based Standards for the selection of health status Measurement Instruments) checklist. BMC Med Res Methodol, 2010, 10: 82.
7.	Mokkink LB, Terwee CB, Patrick DL, et al. The COSMIN study reached international consensus on taxonomy, terminology, and definitions of measurement properties for health-related patient-reported outcomes. J Clin Epidemiol, 2010, 63(7): 737-745.
8.	Terwee CB, Prinsen CAC, Chiarotto A, et al. COSMIN methodology for evaluating the content validity of patient-reported outcome measures: a Delphi study. Qual Life Res, 2018, 27(5): 1159-1170.
9.	Chiarotto A, Ostelo RW, Boers M, et al. A systematic review highlights the need to investigate the content validity of patient-reported outcome measures for physical functioning in patients with low back pain. J Clin Epidemiol, 2018, 95: 73-93.
10.	Fayers PM, Hand DJ, Bjordal K, et al. Causal indicators in quality of life research. Qual Life Res, 1997, 6(5): 393-406.
11.	de Vet HC, Terwee CB, Mokkink LB, et al. Measurement in medicine. Cambridge: Cambridge University Press, 2011.
12.	Floyd FJ, Widaman KF. Factor analysis in the development and refinement of clinical assessment instruments. Psychol Assess, 1995, 7(3): 286-299.
13.	Comrey AL, Lee HB. A first course in factor analysis (2^nd edition). New York: Psychology Press, 1992.
14.	Embretson SE, Reise SP. Item response theory for psychologists. New Jersey: Lawrence Erlbaum Associates, 2000.
15.	Brown TA. Confirmatory factor analysis for applied research. New York: The Guilford Press, 2015.
16.	Edelen MO, Reeve BB. Applying item response theory (IRT) modeling to questionnaire development, evaluation, and refinement. Qual Life Res, 2007, 16: 5-18.
17.	de Vet HC, Adèr HJ, Terwee CB, et al. Are factor analytical techniques used appropriately in the validation of health status questionnaires? A systematic review on the quality of factor analysis of the SF-36. Qual Life Res, 2005, 14(5): 1203-1218.
18.	Revelle W, Zinbarg RE. Coefficients alpha, beta, omega, and the glb: comments on Sijtsma. Psychometrika, 2009, 74: 145.
19.	Scott NW, Fayers PM, Aaronson NK, et al. A simulation study provided sample size guidance for differential item functioning (DIF) studies using short scales. J Clin Epidemiol, 2009, 62(3): 288-295.

1. Prinsen CAC, Mokkink LB, Bouter LM, et al. COSMIN guideline for systematic reviews of patient-reported outcome measures. Qual Life Res, 2018, 27(5): 1147-1157.
2. Mokkink LB, Terwee CB, Patrick DL, et al. The COSMIN checklist for assessing the methodological quality of studies on measurement properties of health status measurement instruments: an international Delphi study. Qual Life Res, 2010, 19(4): 539-549.
3. Mokkink LB, de Vet HCW, Prinsen CAC, et al. COSMIN Risk of Bias checklist for systematic reviews of patient-reported outcome measures. Qual Life Res, 2018, 27(5): 1171-1179.
4. 程玲灵, 孙玉倩. 癌症患者自我管理测评量表的编制及信效度检验. 中华护理杂志, 2017, 52(9): 1082-1087.
5. 林少炜, 李煌元, 赵璧, 等. 亚健康量表中医生和护士的测量不变性. 卫生研究, 2018, 47(3): 399-402.
6. Mokkink LB, Terwee CB, Gibbons E, et al. Inter-rater agreement and reliability of the COSMIN (COnsensus-based Standards for the selection of health status Measurement Instruments) checklist. BMC Med Res Methodol, 2010, 10: 82.
7. Mokkink LB, Terwee CB, Patrick DL, et al. The COSMIN study reached international consensus on taxonomy, terminology, and definitions of measurement properties for health-related patient-reported outcomes. J Clin Epidemiol, 2010, 63(7): 737-745.
8. Terwee CB, Prinsen CAC, Chiarotto A, et al. COSMIN methodology for evaluating the content validity of patient-reported outcome measures: a Delphi study. Qual Life Res, 2018, 27(5): 1159-1170.
9. Chiarotto A, Ostelo RW, Boers M, et al. A systematic review highlights the need to investigate the content validity of patient-reported outcome measures for physical functioning in patients with low back pain. J Clin Epidemiol, 2018, 95: 73-93.
10. Fayers PM, Hand DJ, Bjordal K, et al. Causal indicators in quality of life research. Qual Life Res, 1997, 6(5): 393-406.
11. de Vet HC, Terwee CB, Mokkink LB, et al. Measurement in medicine. Cambridge: Cambridge University Press, 2011.
12. Floyd FJ, Widaman KF. Factor analysis in the development and refinement of clinical assessment instruments. Psychol Assess, 1995, 7(3): 286-299.
13. Comrey AL, Lee HB. A first course in factor analysis (2^nd edition). New York: Psychology Press, 1992.
14. Embretson SE, Reise SP. Item response theory for psychologists. New Jersey: Lawrence Erlbaum Associates, 2000.
15. Brown TA. Confirmatory factor analysis for applied research. New York: The Guilford Press, 2015.
16. Edelen MO, Reeve BB. Applying item response theory (IRT) modeling to questionnaire development, evaluation, and refinement. Qual Life Res, 2007, 16: 5-18.
17. de Vet HC, Adèr HJ, Terwee CB, et al. Are factor analytical techniques used appropriately in the validation of health status questionnaires? A systematic review on the quality of factor analysis of the SF-36. Qual Life Res, 2005, 14(5): 1203-1218.
18. Revelle W, Zinbarg RE. Coefficients alpha, beta, omega, and the glb: comments on Sijtsma. Psychometrika, 2009, 74: 145.
19. Scott NW, Fayers PM, Aaronson NK, et al. A simulation study provided sample size guidance for differential item functioning (DIF) studies using short scales. J Clin Epidemiol, 2009, 62(3): 288-295.

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