Statistical methods in pragmatic randomized controlled trials (I): addressing non-compliance_Chinese Journal of Evidence-Based Medicine

Authors：

SHI Qingyang ¹ , LI Ling ¹ , LI Sheyu ^1,2 ,  SUN Xin ¹

1. Chinese Evidence-based Medicine Center, West China Hospital, Sichuan University, Chengdu 610041, P.R.China;
2. Department of Endocrinology and Metabolism, West China Hospital, Sichuan University, Chengdu 610041, P.R.China;

Corresponding author：

SUN Xin, Email: sunxin@wchscu.cn

Keywords：

Pragmatic randomized controlled trial; Compliance; Statistical method; ITT analysis

DOI：

10.7507/1672-2531.202010019

Video：

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In the study of real-world data, the pragmatic randomized controlled trial can provide the optimal evidence for clinical decisions. Although randomization protects against confounding, post-randomization confounding may still arise due to non-compliance. Traditional intention-to-treat analysis will drift apart from true estimation and lead to deviation of clinical decisions. Meanwhile, the alternative traditional methods would subject to bias and confounding. Thus, new methods are required for revolution, i.e., instrument variable method and modern per-protocol analysis. Our study reviews the defects of traditional methods in pragmatic randomized controlled trials, and then refers to two new methods with a detailed discussion of strengths and weaknesses. We aim to provide researches with insights on choosing the statistical methods for pragmatic trial.

Citation： SHI Qingyang, LI Ling, LI Sheyu, SUN Xin. Statistical methods in pragmatic randomized controlled trials (I): addressing non-compliance. Chinese Journal of Evidence-Based Medicine, 2021, 21(1): 117-124. doi: 10.7507/1672-2531.202010019 Copy

1.	Sherman RE, Anderson SA, Dal Pan GJ, et al. Real-World Evidence-What is it and what can it tell us? N Engl J Med, 2016, 375(23): 2293-2297.
2.	Sun X, Tan J, Tang L, et al. Real world evidence: experience and lessons from China. BMJ, 2018, 360: j5262.
3.	温泽淮, 李玲, 刘艳梅, 等. 实效性随机对照试验的技术规范. 中国循证医学杂志, 2019, 19(7): 794-802.
4.	Zuidgeest MGP, Goetz I, Groenwold RHH, et al. Series: Pragmatic trials and real world evidence: Paper 1. Introduction. J Clin Epidemiol, 2017, 88: 7-13.
5.	Gamerman V, Cai T, Elsässer A. Pragmatic randomized clinical trials: best practices and statistical guidance. Health Serv Outcomes Res Methodol, 2019, 19(1): 23-35.
6.	Hernán MA, Hernández-Díaz S, Robins JM. Randomized trials analyzed as observational studies. Ann Intern Med, 2013, 159(8): 560-562.
7.	Montori VM, Guyatt GH. Intention-to-treat principle. CMAJ, 2001, 165(10): 1339-1341.
8.	Angrist JD, Imbens GW, Rubin DB. Identification of causal effects using instrumental variables. J Am Stat Assoc, 1996, 91(434): 444-455.
9.	Hernán MA, Robins JM. Per-protocol analyses of pragmatic trials. N Engl J Med, 2017, 377(14): 1391-1398.
10.	Hernán MA, Hernández-Díaz S. Beyond the intention-to-treat in comparative effectiveness research. Clin Trials, 2012, 9(1): 48-55.
11.	Liao JM, Stack CB, Griswold ME, et al. Annals understanding clinical research: intention-to-treat analysis. Ann Intern Med, 2017, 166(9): 662-664.
12.	Shrier I, Verhagen E, Stovitz SD. The intention-to-treat analysis is not always the conservative approach. Am J Med, 2017, 130(7): 867-871.
13.	Hernán MA, Scharfstein D. Cautions as regulators move to end exclusive reliance on intention to treat. Ann Intern Med, 2018, 168(7): 515-516.
14.	Murray EJ, Caniglia EC, Swanson SA, et al. Patients and investigators prefer measures of absolute risk in subgroups for pragmatic randomized trials. J Clin Epidemiol, 2018, 103: 10-21.
15.	Mansournia MA, Higgins JP, Sterne JA, et al. Biases in randomized trials: a conversation between trialists and epidemiologists. Epidemiology, 2017, 28(1): 54-59.
16.	Hernán MA, Hernández-Díaz S, Robins JM. A structural approach to selection bias. Epidemiology, 2004, 15(5): 615-625.
17.	Greenland S. An introduction to instrumental variables for epidemiologists. Int J Epidemiol, 2000, 29(4): 722-729.
18.	Hernán MA, Robins JM. Instruments for causal inference: an epidemiologist's dream? Epidemiology, 2006, 17(4): 360-372.
19.	Wang L, Tchetgen Tchetgen E. Bounded, efficient and multiply robust estimation of average treatment effects using instrumental variables. J R Stat Soc Series B Stat Methodol, 2018, 80(3): 531-550.
20.	Swanson SA, Hernán MA. Commentary: how to report instrumental variable analyses (suggestions welcome). Epidemiology, 2013, 24(3): 370-374.
21.	Vansteelandt S, Goetghebeur E. Causal inference with generalized structural mean models. J R Stat Soc Series B Stat Methodol, 2003, 65(4): 817-835.
22.	Baiocchi M, Cheng J, Small DS. Instrumental variable methods for causal inference. Stat Med, 2014, 33(13): 2297-2340.
23.	Robins J. A new approach to causal inference in mortality studies with a sustained exposure periodapplication to control of the healthy worker survivor effect. Math Model, 1986, 7(9): 1393-1512.
24.	Robins JM. Causal inference from complex longitudinal data. Berkane M, ed. Latent variable modeling and applications to causality. New York: Springer, 1997: 69-117.
25.	Greenland S. Quantifying biases in causal models: classical confounding vs collider-stratification bias. Epidemiology, 2003, 14(3): 300-306.
26.	Naimi AI, Cole SR, Kennedy EH. An introduction to g methods. Int J Epidemiol, 2017, 46(2): 756-762.
27.	Robins JM, Hernán MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology, 2000, 11(5): 550-560.

1. Sherman RE, Anderson SA, Dal Pan GJ, et al. Real-World Evidence-What is it and what can it tell us? N Engl J Med, 2016, 375(23): 2293-2297.
2. Sun X, Tan J, Tang L, et al. Real world evidence: experience and lessons from China. BMJ, 2018, 360: j5262.
3. 温泽淮, 李玲, 刘艳梅, 等. 实效性随机对照试验的技术规范. 中国循证医学杂志, 2019, 19(7): 794-802.
4. Zuidgeest MGP, Goetz I, Groenwold RHH, et al. Series: Pragmatic trials and real world evidence: Paper 1. Introduction. J Clin Epidemiol, 2017, 88: 7-13.
5. Gamerman V, Cai T, Elsässer A. Pragmatic randomized clinical trials: best practices and statistical guidance. Health Serv Outcomes Res Methodol, 2019, 19(1): 23-35.
6. Hernán MA, Hernández-Díaz S, Robins JM. Randomized trials analyzed as observational studies. Ann Intern Med, 2013, 159(8): 560-562.
7. Montori VM, Guyatt GH. Intention-to-treat principle. CMAJ, 2001, 165(10): 1339-1341.
8. Angrist JD, Imbens GW, Rubin DB. Identification of causal effects using instrumental variables. J Am Stat Assoc, 1996, 91(434): 444-455.
9. Hernán MA, Robins JM. Per-protocol analyses of pragmatic trials. N Engl J Med, 2017, 377(14): 1391-1398.
10. Hernán MA, Hernández-Díaz S. Beyond the intention-to-treat in comparative effectiveness research. Clin Trials, 2012, 9(1): 48-55.
11. Liao JM, Stack CB, Griswold ME, et al. Annals understanding clinical research: intention-to-treat analysis. Ann Intern Med, 2017, 166(9): 662-664.
12. Shrier I, Verhagen E, Stovitz SD. The intention-to-treat analysis is not always the conservative approach. Am J Med, 2017, 130(7): 867-871.
13. Hernán MA, Scharfstein D. Cautions as regulators move to end exclusive reliance on intention to treat. Ann Intern Med, 2018, 168(7): 515-516.
14. Murray EJ, Caniglia EC, Swanson SA, et al. Patients and investigators prefer measures of absolute risk in subgroups for pragmatic randomized trials. J Clin Epidemiol, 2018, 103: 10-21.
15. Mansournia MA, Higgins JP, Sterne JA, et al. Biases in randomized trials: a conversation between trialists and epidemiologists. Epidemiology, 2017, 28(1): 54-59.
16. Hernán MA, Hernández-Díaz S, Robins JM. A structural approach to selection bias. Epidemiology, 2004, 15(5): 615-625.
17. Greenland S. An introduction to instrumental variables for epidemiologists. Int J Epidemiol, 2000, 29(4): 722-729.
18. Hernán MA, Robins JM. Instruments for causal inference: an epidemiologist's dream? Epidemiology, 2006, 17(4): 360-372.
19. Wang L, Tchetgen Tchetgen E. Bounded, efficient and multiply robust estimation of average treatment effects using instrumental variables. J R Stat Soc Series B Stat Methodol, 2018, 80(3): 531-550.
20. Swanson SA, Hernán MA. Commentary: how to report instrumental variable analyses (suggestions welcome). Epidemiology, 2013, 24(3): 370-374.
21. Vansteelandt S, Goetghebeur E. Causal inference with generalized structural mean models. J R Stat Soc Series B Stat Methodol, 2003, 65(4): 817-835.
22. Baiocchi M, Cheng J, Small DS. Instrumental variable methods for causal inference. Stat Med, 2014, 33(13): 2297-2340.
23. Robins J. A new approach to causal inference in mortality studies with a sustained exposure periodapplication to control of the healthy worker survivor effect. Math Model, 1986, 7(9): 1393-1512.
24. Robins JM. Causal inference from complex longitudinal data. Berkane M, ed. Latent variable modeling and applications to causality. New York: Springer, 1997: 69-117.
25. Greenland S. Quantifying biases in causal models: classical confounding vs collider-stratification bias. Epidemiology, 2003, 14(3): 300-306.
26. Naimi AI, Cole SR, Kennedy EH. An introduction to g methods. Int J Epidemiol, 2017, 46(2): 756-762.
27. Robins JM, Hernán MA, Brumback B. Marginal structural models and causal inference in epidemiology. Epidemiology, 2000, 11(5): 550-560.

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