Overview and perspectives on risk of bias assessment tools in network meta-analysis_Chinese Journal of Evidence-Based Medicine

Authors：

ZHENG Qingyong ^1,2 , XU Jianguo ^1,2 , LIU Ming ^1,2 , ZHOU Yongjia ^1,2,3 , LI Tengfei ^1,2,3 , YUAN Kaisen ¹ , ZHANG Ye ⁴ , TANG Xiao ⁴ , LIU Jia ⁵ , LI Molan ⁴ , WANG Na ⁴ , WANG Gang ⁶ ,  ZHANG Junhua ^7,8 ,  TIAN Jinhui ^1,2

1. Evidence-Based Medicine Center, School of Basic Medical Sciences, Lanzhou University, Lanzhou 730000, P. R. China;
2. Key Laboratory of Evidence-based Medicine of Gansu Province, Lanzhou 730000, P. R. China;
3. School of Nursing, Gansu University of Chinese Medicine, Lanzhou 730000, P. R. China;
4. The First Clinical School of Medicine, Lanzhou University, Lanzhou 730000, P. R. China;
5. The Second Clinical Medical College of Lanzhou University, Lanzhou 730000, P. R. China;
6. Gansu Provincial Hospital of Traditional Chinese Medicine, Lanzhou 730050, P. R. China;
7. Evidence-Based Medicine Center, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, P. R. China;
8. Key Laboratory of Evidence-Based Evaluation of Traditional Chinese Medicine, National Medical Products Administration, Tianjin 301617, P. R. China;

Corresponding author：

ZHANG Junhua, Email: zjhtcm@foxmail.com; TIAN Jinhui, Email: tjh996@163.com

Keywords：

Network meta-analysis; Risk bias assessment tools; Methodology; Challenges

DOI：

10.7507/1672-2531.202411077

Video：

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Accurately assessing the risk of bias is a critical challenge in network meta-analysis (NMA). By integrating direct and indirect evidence, NMA enables the comparison of multiple interventions, but its outcomes are often influenced by bias risks, particularly the propagation of bias within complex evidence networks. This paper systematically reviews commonly used bias risk assessment tools in NMA, highlighting their applications, limitations, and challenges across interventional trials, observational studies, diagnostic tests, and animal experiments. Addressing the issues of tool misapplication, mixed usage, and the lack of comprehensive tools for overall bias assessment in NMA, we propose strategies such as simplifying tool operation, enhancing usability, and standardizing evaluation processes. Furthermore, advancements in artificial intelligence (AI) and large language models (LLMs) offer promising opportunities to streamline bias risk assessments and reduce human interference. The development of specialized tools and the integration of intelligent technologies will enhance the rigor and reliability of NMA studies, providing robust evidence to support medical research and clinical decision-making.

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1. 曾宪涛, 曹世义, 孙凤, 等. Meta分析系列之六: 间接比较及网状分析. 中国循证心血管医学杂志, 2012, 4(5): 399-402.
2. Song F, Harvey I, Lilford R. Adjusted indirect comparison may be less biased than direct comparison for evaluating new pharmaceutical interventions. J Clin Epidemiol, 2008, 61(5): 455-463.
3. 井俊凯, 胡晓婷, 时嘉彤, 等. 3种改善血液循环药物联合基础治疗用于急性脑梗死的网状Meta分析及药物经济学评价. 药物评价研究, 2021, 44(3): 617-627.
4. Ades AE, Welton NJ, Dias S, et al. Twenty years of network meta-analysis: continuing controversies and recent developments. Res Synth Methods, 2024, 15(5): 702-727.
5. 吴景玲, 葛龙, 张俊华, 等. 多个诊断性试验准确性的比较: 网状Meta分析方法介绍. 中国循证医学杂志, 2017, 17(8): 987-992.
6. Faron M, Cheugoua-Zanetsie M, Tierney J, et al. Individual participant data network meta-analysis of neoadjuvant chemotherapy or chemoradiotherapy in esophageal or gastroesophageal junction carcinoma. J Clin Oncol, 2023, 41(28): 4535-4547.
7. Couturier A, Calissi C, Cracowski JL, et al. Mouse models of diabetes-related ulcers: a systematic review and network meta-analysis. EBioMedicine, 2023, 98: 104856.
8. Jeon J, Park SH, Choi J, et al. Association between neural stem/progenitor cells and biomaterials in spinal cord injury therapies: a systematic review and network meta-analysis. Acta Biomater, 2024, 183: 50-60.
9. 张方圆, 沈傲梅, 曾宪涛, 等. 系统评价方法学质量评价工具AMSTAR2解读. 中国循证心血管医学杂志, 2018, 10(1): 14-18.
10. 王瑞平. 临床研究中混杂偏倚的识别和控制策略. 上海医药, 2022, 43(15): 30-34.
11. Higgins JP, Altman DG, Gøtzsche PC, et al. The Cochrane Collaboration's tool for assessing risk of bias in randomised trials. BMJ, 2011, 343: d5928.
12. Sterne JAC, Savović J, Page MJ, et al. RoB 2: a revised tool for assessing risk of bias in randomised trials. BMJ, 2019, 366: l4898.
13. Whiting PF, Rutjes AW, Westwood ME, et al. QUADAS-2: a revised tool for the quality assessment of diagnostic accuracy studies. Ann Intern Med, 2011, 155(8): 529-536.
14. Schünemann HJ, Cuello C, Akl EA, et al. GRADE guidelines: 18. How ROBINS-I and other tools to assess risk of bias in nonrandomized studies should be used to rate the certainty of a body of evidence. J Clin Epidemiol, 2019, 111: 105-114.
15. 黄悦勤. 医学科研中随机误差控制和样本量确定. 中国心理卫生杂志, 2015, 29(11): 874-880.
16. 李柄辉, 訾豪, 李路遥, 等. 医学领域一次研究和二次研究的方法学质量(偏倚风险)评价工具. 医学新知, 2021, 31(1): 51-58.
17. 沈洪兵, 齐秀英. 流行病学(第9版). 北京: 人民卫生出版社, 2018.
18. Detsky AS, Naylor CD, O'Rourke K, et al. Incorporating variations in the quality of individual randomized trials into meta-analysis. J Clin Epidemiol, 1992, 45(3): 255-265.
19. Liu W, Duan Y, Cui W, et al. Skin antiseptics in venous puncture site disinfection for preventing blood culture contamination: a Bayesian network meta-analysis of randomized controlled trials. Int J Nurs Stud, 2016, 59: 156-162.
20. Critical Appraisal Skill Programme. CASP checklist: CASP randomised controlled trial checklist. 2023.
21. Chou PL, Huang YP, Cheng MH, et al. Improvement of paclitaxel-associated adverse reactions (ADRs) via the use of nano-based drug delivery systems: a systematic review and network meta-analysis. Int J Nanomedicine, 2020, 15: 1731-1743.
22. Jadad AR, Moore RA, Carroll D, et al. Assessing the quality of reports of randomized clinical trials: is blinding necessary. Control Clin Trials, 1996, 17(1): 1-12.
23. Wong AYW, Hooi NMF, Yeo BSY, et al. Improving diabetic wound-healing outcomes with topical growth factor therapies. J Clin Endocrinol Metab, 2024, 109(8): e1642-e1651.
24. Maher CG, Sherrington C, Herbert RD, et al. Reliability of the PEDro scale for rating quality of randomized controlled trials. Physical therapy, 2003, 83(8): 713-721.
25. Binesh M, Ehsani F, Motaharinezhad F, et al. The effect of whole-body vibration on glucose and lipid profiles in type-2 diabetes: a systematic review and pairwise and network meta-analyses of randomized trials. Sci Rep, 2024, 14(1): 12494.
26. Chen Z, Tian F. Evaluation of oral small molecule drugs for the treatment of COVID-19 patients: a systematic review and network meta-analysis. Ann Med, 2023, 55(2): 2274511.
27. National Heart, Lung, and Blood Institute. Study quality assessment tools. 2023.
28. Shaffer A, Naik A, Bederson M, et al. Efficacy of deep brain stimulation for the treatment of anorexia nervosa: a systematic review and network meta-analysis of patient-level data. Neurosurg Focus, 2023, 54(2): E5.
29. Carron M, Tamburini E, Linassi F, et al. Efficacy of nonopioid analgesics and adjuvants in multimodal analgesia for reducing postoperative opioid consumption and complications in obesity: a systematic review and network meta-analysis. Br J Anaesth, 2024, 133(6): 1234-1249.
30. Barker TH, Stone JC, Sears K, et al. The revised JBI critical appraisal tool for the assessment of risk of bias for randomized controlled trials. JBI Evid Synth, 2023, 21(3): 494-506.
31. Rossi MT, de Oliveira MN, Vidigal MTC, et al. Effectiveness of anesthetic solutions for pain control in lower third molar extraction surgeries: a systematic review of randomized clinical trials with network meta-analysis. Clin Oral Investig, 2021, 25(1): 1-22.
32. Downs SH, Black N. The feasibility of creating a checklist for the assessment of the methodological quality both of randomised and non-randomised studies of health care interventions. J Epidemiol Community Health, 1998, 52(6): 377-384.
33. McNulty KL, Elliott-Sale KJ, Dolan E, et al. The effects of menstrual cycle phase on exercise performance in Eumenorrheic women: a systematic review and meta-analysis. Sports Med, 2020, 50(10): 1813-1827.
34. Slim K, Nini E, Forestier D, et al. Methodological index for non-randomized studies (minors): development and validation of a new instrument. ANZ J Surg, 2003, 73(9): 712-716.
35. Ji Z, Hu H, Qiang X, et al. Traditional Chinese medicine for COVID-19: a network meta-analysis and systematic review. Am J Chin Med, 2022, 50(4): 883-925.
36. Sterne JA, Hernán MA, Reeves BC, et al. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ, 2016, 355: i4919.
37. Billeter AT, Reiners B, Seide SE, et al. Comparative effectiveness of medical treatment vs. metabolic surgery for histologically proven non-alcoholic steatohepatitis and fibrosis: a matched network meta-analysis. Hepatobiliary Surg Nutr, 2022, 11(5): 696-708.
38. Barker TH, Habibi N, Aromataris E, et al. The revised JBI critical appraisal tool for the assessment of risk of bias for quasi-experimental studies. JBI Evid Synth, 2024, 22(3): 378-388.
39. Huang J, Forde R, Parsons J, et al. Interventions to increase the uptake of postpartum diabetes screening among women with previous gestational diabetes: a systematic review and Bayesian network meta-analysis. Am J Obstet Gynecol MFM, 2023, 5(10): 101137.
40. Stang A. Critical evaluation of the Newcastle-Ottawa scale for the assessment of the quality of nonrandomized studies in meta-analyses. Eur J Epidemiol, 2010, 25(9): 603-605.
41. Low CJW, Ling RR, Lau MPXL, et al. Mechanical circulatory support for cardiogenic shock: a network meta-analysis of randomized controlled trials and propensity score-matched studies. Intensive Care Med, 2024, 50(2): 209-221.
42. Hayden JA, van der Windt DA, Cartwright JL, et al. Assessing bias in studies of prognostic factors. Ann Intern Med, 2013, 158(4): 280-286.
43. Vámos O, Komora P, Gede N, et al. The effect of nicotine-containing products on peri-implant tissues: a systematic review and network meta-analysis. Nicotine Tob Res, 2024, 26(10): 1276-1285.
44. Pesce P, Menini M, Ugo G, et al. Evaluation of periodontal indices among non-smokers, tobacco, and e-cigarette smokers: a systematic review and network meta-analysis. Clin Oral Investig, 2022, 26(7): 4701-4714.
45. Moola SMZ, Tufanaru C, Aromataris E, et al. Chapter 7: systematic reviews of etiology and risk . In: Aromataris E, Munn Z (Editors). JBI manual for evidence synthesis. JBI, 2020.
46. Okeahialam NA, Taithongchai A, Thakar R, et al. The incidence of anal incontinence following obstetric anal sphincter injury graded using the Sultan classification: a network meta-analysis. Am J Obstet Gynecol, 2023, 228(6): 675-688.
47. Higgins JPT, Morgan RL, Rooney AA, et al. A tool to assess risk of bias in non-randomized follow-up studies of exposure effects (ROBINS-E). Environ Int, 2024, 186: 108602.
48. Wallerer S, Papakonstantinou T, Morze J, et al. Association between substituting macronutrients and all-cause mortality: a network meta-analysis of prospective observational studies. EClinicalMedicine, 2024, 75: 102807.
49. 魏来武, 陈削静, 曹钰彬, 等. 暴露类非随机研究偏倚风险评价工具(ROBINS-E2022版)解读. 中国循证医学杂志, 2023, 23(11): 1340-1350.
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Chinese Journal of Evidence-Based Medicine

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