Progress and application of quantitative bias analysis methods in real-world study_Chinese Journal of Evidence-Based Medicine

Authors：

JIANG Haoxuan ¹ , CHEN Keer ² ,  WU Ying ³

1. School of Biomedical Engineering, Hainan University, Sanya 572024, P. R. China;
2. Hainan Lecheng Institute of Real World Study, the Administration of Boao Lecheng International Medical Tourism Pilot Zone, Hainan 571437, P. R. China;
3. Department of Biostatistics, School of Public Health, Southern Medical University, Guangzhou 510515, P. R. China;

Corresponding?author：

WU Ying, Email: wuying19890321@gmail.com

Keywords：

Real-world study; Quantitative bias analysis; Unmeasured confounding bias; Information bias; Selection bias

DOI：

10.7507/1672-2531.202508029

Video：

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Real-world studies (RWS) can more accurately reflect patient treatment outcomes and long-term prognosis in clinical practice, and they play an increasingly important role in drug effectiveness evaluation and regulatory decision-making. However, due to their non-randomized nature, RWS are susceptible to systematic biases - such as unmeasured (or uncontrolled) confounding bias, information bias (e.g., measurement error or misclassification), and selection bias - which may lead to deviations from the true effect and compromise the reliability of evidence and the rationality of policy decisions. Quantitative bias analysis (QBA) is a methodological approach used to assess the impact of bias on study results, enabling the quantification of the direction, magnitude, and uncertainty of such biases. To promote the standardized application of QBA in real-world research, this paper systematically reviews existing QBA methods and their applicable scenarios, aiming to provide methodological references and practical guidance for researchers and decision-makers in improving the interpretability and credibility of real-world evidence.

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4.	Lash TL, Fox MP, MacLehose RF, et al. Good practices for quantitative bias analysis. Int J Epidemiol, 2014, 43(6): 1969-1985.
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6.	Evans JM, Donnelly LA, Emslie-Smith AM, et al. Metformin and reduced risk of cancer in diabetic patients. BMJ, 2005, 330(7503): 1304-1305.
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9.	Suissa S, Azoulay L. Metformin and the risk of cancer: time-related biases in observational studies. Diabetes Care, 2012, 35(12): 2665-2673.
10.	Brown JP, Hunnicutt JN, Ali MS, et al. Core concepts in pharmacoepidemiology: quantitative bias analysis. Pharmacoepidemiol Drug Saf, 2024, 33(10): e70026.
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12.	Ding P, VanderWeele TJ. Sensitivity analysis without assumptions. Epidemiology, 2016, 27(3): 368-377.
13.	Victora CG, Smith PG, Vaughan JP, et al. Evidence for protection by breast-feeding against infant deaths from infectious diseases in Brazil. Lancet, 1987, 2(8554): 319-322.
14.	VanderWeele TJ, Ding P. Sensitivity analysis in observational research: introducing the E-value. Ann Intern Med, 2017, 167(4): 268-274.
15.	Yang Q, Yang Z, Cai X, et al. Advances in methodologies of negative controls: a scoping review. J Clin Epidemiol, 2024, 166: 111228.
16.	Sofer T, Richardson DB, Colicino E, et al. On negative outcome control of unobserved confounding as a generalization of difference-in-differences. Stat Sci, 2016, 31(3): 348-361.
17.	Tchetgen Tchetgen EJ, Park C, Richardson DB. Universal difference-in-differences for causal inference in epidemiology. Epidemiology, 2024, 35(1): 16-22.
18.	Rasmussen SA, Jamieson DJ, Honein MA, et al. Zika virus and birth defects-reviewing the evidence for causality. N Engl J Med, 2016, 374(20): 1981-1987.
19.	Diaz-Quijano FA, Pelissari DM, Chiavegatto Filho ADP. Zika-associated microcephaly epidemic and birth rate reduction in Brazilian cities. Am J Public Health, 2018, 108(4): 514-516.
20.	Miao W, Shi X, Li Y, et al. A confounding bridge approach for double negative control inference on causal effects. 2024.
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24.	Tyndall MW, Ronald AR, Agoki E, et al. Increased risk of infection with human immunodeficiency virus type 1 among uncircumcised men presenting with genital ulcer disease in Kenya. Clin Infect Dis, 1996, 23(3): 449-453.
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26.	Bowker SL, Majumdar SR, Veugelers P, et al. Increased cancer-related mortality for patients with type 2 diabetes who use sulfonylureas or insulin. Diabetes Care, 2006, 29(2): 254-258.
27.	Karim ME, Gustafson P, Petkau J, et al. Comparison of statistical approaches for dealing with immortal time bias in drug effectiveness studies. Am J Epidemiol, 2016, 184(4): 325-335.
28.	王振宇, 陳朔華, 趙欣宇, 等. Cox及其拓展模型在基于隊列的依時暴露因素效應估計中的應用. 中華流行病學雜志, 2020, 41(6): 957-961.
29.	Gray C, Ralphs E, Fox MP, et al. Use of quantitative bias analysis to evaluate single-arm trials with real-world data external controls. Pharmacoepidemiol Drug Saf, 2024, 33(5): e5796.
30.	Desai RJ, Levin R, Lin KJ, et al. Bias implications of outcome misclassification in observational studies evaluating association between treatments and all-cause or cardiovascular mortality using administrative claims. J Am Heart Assoc, 2020, 9(17): e016906.
31.	劉子言, 吳小麗, 解美秋, 等. 在因果推斷中應用有向無環圖識別和控制選擇偏倚. 中華疾病控制雜志, 2019, 23(3): 351-355.
32.	Stang A, Schmidt-Pokrzywniak A, Lash TL, et al. Mobile phone use and risk of uveal melanoma: results of the risk factors for uveal melanoma case-control study. J Natl Cancer Inst, 2009, 101(2): 120-123.
33.	Nohr EA, Liew Z. How to investigate and adjust for selection bias in cohort studies. Acta Obstet Gynecol Scand, 2018, 97(4): 407-416.
34.	Lash TL, Silliman RA, Guadagnoli E, et al. The effect of less than definitive care on breast carcinoma recurrence and mortality. Cancer, 2000, 89(8): 1739-1747.
35.	Sun B, Perkins NJ, Cole SR, et al. Inverse-probability-weighted estimation for monotone and nonmonotone missing data. Am J Epidemiol, 2018, 187(3): 585-591.
36.	Cornfield J, Haenszel W, Hammond EC, et al. Smoking and lung cancer: recent evidence and a discussion of some questions. 1959. Int J Epidemiol, 2009, 38(5): 1175-1191.

1. Petersen JM, Ranker LR, Barnard-Mayers R, et al. A systematic review of quantitative bias analysis applied to epidemiological research. Int J Epidemiol, 2021, 50(5): 1708-1730.
2. Bosco JL, Silliman RA, Thwin SS, et al. A most stubborn bias: no adjustment method fully resolves confounding by indication in observational studies. J Clin Epidemiol, 2010, 63(1): 64-74.
3. Lash TL, Fink AK, Fox MP. Data sources for bias analysis. 2009.
4. Lash TL, Fox MP, MacLehose RF, et al. Good practices for quantitative bias analysis. Int J Epidemiol, 2014, 43(6): 1969-1985.
5. Turkiewicz A, Nilsson PM, Kiadaliri A. Probabilistic quantification of bias to combine the strengths of population-based register data and clinical cohorts-studying mortality in osteoarthritis. Am J Epidemiol, 2020, 189(12): 1590-1599.
6. Evans JM, Donnelly LA, Emslie-Smith AM, et al. Metformin and reduced risk of cancer in diabetic patients. BMJ, 2005, 330(7503): 1304-1305.
7. Decensi A, Puntoni M, Goodwin P, et al. Metformin and cancer risk in diabetic patients: a systematic review and meta-analysis. Cancer Prev Res (Phila), 2010, 3(11): 1451-1461.
8. Noto H, Goto A, Tsujimoto T, et al. Cancer risk in diabetic patients treated with metformin: a systematic review and meta-analysis. PLoS One, 2012, 7(3): e33411.
9. Suissa S, Azoulay L. Metformin and the risk of cancer: time-related biases in observational studies. Diabetes Care, 2012, 35(12): 2665-2673.
10. Brown JP, Hunnicutt JN, Ali MS, et al. Core concepts in pharmacoepidemiology: quantitative bias analysis. Pharmacoepidemiol Drug Saf, 2024, 33(10): e70026.
11. 國家藥監局藥審中心. 關于發布《藥物真實世界研究設計與方案框架指導原則(試行)》的通告(2023年第5號). 2023.
12. Ding P, VanderWeele TJ. Sensitivity analysis without assumptions. Epidemiology, 2016, 27(3): 368-377.
13. Victora CG, Smith PG, Vaughan JP, et al. Evidence for protection by breast-feeding against infant deaths from infectious diseases in Brazil. Lancet, 1987, 2(8554): 319-322.
14. VanderWeele TJ, Ding P. Sensitivity analysis in observational research: introducing the E-value. Ann Intern Med, 2017, 167(4): 268-274.
15. Yang Q, Yang Z, Cai X, et al. Advances in methodologies of negative controls: a scoping review. J Clin Epidemiol, 2024, 166: 111228.
16. Sofer T, Richardson DB, Colicino E, et al. On negative outcome control of unobserved confounding as a generalization of difference-in-differences. Stat Sci, 2016, 31(3): 348-361.
17. Tchetgen Tchetgen EJ, Park C, Richardson DB. Universal difference-in-differences for causal inference in epidemiology. Epidemiology, 2024, 35(1): 16-22.
18. Rasmussen SA, Jamieson DJ, Honein MA, et al. Zika virus and birth defects-reviewing the evidence for causality. N Engl J Med, 2016, 374(20): 1981-1987.
19. Diaz-Quijano FA, Pelissari DM, Chiavegatto Filho ADP. Zika-associated microcephaly epidemic and birth rate reduction in Brazilian cities. Am J Public Health, 2018, 108(4): 514-516.
20. Miao W, Shi X, Li Y, et al. A confounding bridge approach for double negative control inference on causal effects. 2024.
21. Park C, Richardson DB, Tchetgen Tchetgen EJ. Single proxy control. Biometrics, 2024, 80(2): ujae027.
22. Lash TL, Fox MP, Fink AK. Applying quantitative bias analysis to epidemiologic data. New York: Springer, 2009.
23. McCandless LC, Gustafson P. A comparison of Bayesian and Monte Carlo sensitivity analysis for unmeasured confounding. Stat Med, 2017, 36(18): 2887-2901.
24. Tyndall MW, Ronald AR, Agoki E, et al. Increased risk of infection with human immunodeficiency virus type 1 among uncircumcised men presenting with genital ulcer disease in Kenya. Clin Infect Dis, 1996, 23(3): 449-453.
25. 柏柳安寧, 夏結來, 王陵, 等. 真實世界研究中的常見偏倚及其控制. 中國臨床藥理學與治療學, 2020, 25(12): 1422-1428.
26. Bowker SL, Majumdar SR, Veugelers P, et al. Increased cancer-related mortality for patients with type 2 diabetes who use sulfonylureas or insulin. Diabetes Care, 2006, 29(2): 254-258.
27. Karim ME, Gustafson P, Petkau J, et al. Comparison of statistical approaches for dealing with immortal time bias in drug effectiveness studies. Am J Epidemiol, 2016, 184(4): 325-335.
28. 王振宇, 陳朔華, 趙欣宇, 等. Cox及其拓展模型在基于隊列的依時暴露因素效應估計中的應用. 中華流行病學雜志, 2020, 41(6): 957-961.
29. Gray C, Ralphs E, Fox MP, et al. Use of quantitative bias analysis to evaluate single-arm trials with real-world data external controls. Pharmacoepidemiol Drug Saf, 2024, 33(5): e5796.
30. Desai RJ, Levin R, Lin KJ, et al. Bias implications of outcome misclassification in observational studies evaluating association between treatments and all-cause or cardiovascular mortality using administrative claims. J Am Heart Assoc, 2020, 9(17): e016906.
31. 劉子言, 吳小麗, 解美秋, 等. 在因果推斷中應用有向無環圖識別和控制選擇偏倚. 中華疾病控制雜志, 2019, 23(3): 351-355.
32. Stang A, Schmidt-Pokrzywniak A, Lash TL, et al. Mobile phone use and risk of uveal melanoma: results of the risk factors for uveal melanoma case-control study. J Natl Cancer Inst, 2009, 101(2): 120-123.
33. Nohr EA, Liew Z. How to investigate and adjust for selection bias in cohort studies. Acta Obstet Gynecol Scand, 2018, 97(4): 407-416.
34. Lash TL, Silliman RA, Guadagnoli E, et al. The effect of less than definitive care on breast carcinoma recurrence and mortality. Cancer, 2000, 89(8): 1739-1747.
35. Sun B, Perkins NJ, Cole SR, et al. Inverse-probability-weighted estimation for monotone and nonmonotone missing data. Am J Epidemiol, 2018, 187(3): 585-591.
36. Cornfield J, Haenszel W, Hammond EC, et al. Smoking and lung cancer: recent evidence and a discussion of some questions. 1959. Int J Epidemiol, 2009, 38(5): 1175-1191.

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