Statistical methods in pragmatic randomized controlled trials (Ⅱ): Addressing missing outcome data_Chinese Journal of Evidence-Based Medicine

Authors：

SHI Qingyang , LI Ling , REN Yan , YAO Minghong ,  SUN Xin

Chinese Evidence-based Medicine Center, West China Hospital, Sichuan University, Chengdu 610041, P.R.China;

Corresponding author：

SUN Xin, Email: sunxin@wchscu.cn

Keywords：

Pragmatic randomized controlled trial; Missing outcome data; Selection bias; Statistical method

DOI：

10.7507/1672-2531.202101140

Video：

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Pragmatic randomized controlled trials can provide high-quality evidence. However, pragmatic trials need to frequently encounter the missing outcome data due to the challenges of quality assurance and control. The missing outcome could lead to bias which may misguide the conclusions. Thus, it is crucial to handle the missing outcome data appropriately. Our study initially summarized the bias structures and missingness mechanisms, and then reviewed important methods based on the assumption of missing at random. We referred to the multiple imputations and inverse probability of censoring weighting for dealing with missing outcomes. This paper aimed to provide insights on how to choose the statistical methods on missing outcome data.

Citation： SHI Qingyang, LI Ling, REN Yan, YAO Minghong, SUN Xin. Statistical methods in pragmatic randomized controlled trials (Ⅱ): Addressing missing outcome data. Chinese Journal of Evidence-Based Medicine, 2021, 21(6): 729-736. doi: 10.7507/1672-2531.202101140 Copy

1.	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.
2.	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.
3.	Irving E, van den Bor R, Welsing P, et al. Series: Pragmatic trials and real world evidence: Paper 7. Safety, quality and monitoring. J Clin Epidemiol, 2017, 91: 6-12.
4.	Montori VM, Guyatt GH. Intention-to-treat principle. CMAJ, 2001, 165(10): 1339-1341.
5.	O'Neill RT, Temple R. The prevention and treatment of missing data in clinical trials: an FDA perspective on the importance of dealing with it. Clin Pharmacol Ther, 2012, 91(3): 550-554.
6.	Little RJ, D'Agostino R, Cohen ML, et al. The prevention and treatment of missing data in clinical trials. N Engl J Med, 2012, 367(14): 1355-1360.
7.	石清阳, 李玲, 李舍予, 等. 实效性随机对照试验中的统计方法(Ⅰ): 解决依从性问题. 中国循证医学杂志, 2021, 21(12): 1-8.
8.	Pearl J. Causal diagrams for empirical research. Biometrika: Oxford University Press, 1995: 669-688.
9.	Hernán MA, Hernández-Díaz S, Robins JM. A structural approach to selection bias. Epidemiology, 2004, 15(5): 615-625.
10.	Greenland S. Response and follow-up bias in cohort studies. Am J Epidemiol, 1977, 106(3): 184-187.
11.	Little RJ, Rubin DB. Statistical analysis with missing data. New York: John Wiley & Sons, 2019: 793.
12.	Rubin DB. Inference and missing data. Biometrika: Oxford University Press, 1976: 581-592.
13.	Knol MJ, Janssen KJ, Donders AR, et al. Unpredictable bias when using the missing indicator method or complete case analysis for missing confounder values: an empirical example. J Clin Epidemiol, 2010, 63(7): 728-736.
14.	Robins JM, Finkelstein DM. Correcting for noncompliance and dependent censoring in an AIDS clinical trial with inverse probability of censoring weighted (IPCW) log-rank tests. Biometrics, 2000, 56(3): 779-788.
15.	Hernán MA, Brumback B, Robins JM. Marginal structural models to estimate the joint causal effect of nonrandomized treatments. J Am Stat Assoc, 2001, 96(454): 440-448.
16.	Saha C, Jones MP. Bias in the last observation carried forward method under informative dropout. J Stat Plan Inference, 2009, 139(2): 246-255.
17.	Horvitz DG, Thompson DJ. A generalization of sampling without replacement from a finite universe. J Am stat Assoc, 1952, 47(260): 663-685.
18.	Rubin DB. Multiple imputation after 18⁺ years. J Am stat Assoc, 1996, 91(434): 473-489.
19.	Van Buuren S. Flexible imputation of missing data (second edition). Boca Raton, FL: CRC Press, 2018.
20.	Herzog TN, Rubin DB. Using multiple imputations to handle nonresponse in sample surveys. New York: Academic Press, 1983: 209-245.
21.	Royston P. Multiple imputation of missing values: further update of ice, with an emphasis on interval censoring. Stata J, 2007, 7(4): 445-464.
22.	Von Hippel PT. How to impute interactions, squares, and other transformed variables. Sociol Method, 2009, 39(1): 265-291.
23.	Van Buuren S, Karin GO. Multivariate imputation by chained equations in R. J Stat Soft, 2011, 45: i03.
24.	Taylor JM, Cooper KL, Wei JT, et al. Use of multiple imputation to correct for nonresponse bias in a survey of urologic symptoms among African-American men. Am J Epidemiol, 2002, 156(8): 774-782.
25.	Hsu CH, Taylor JM, Murray S, et al. Survival analysis using auxiliary variables via non-parametric multiple imputation. Stat Med, 2006, 25(20): 3503-3517.
26.	Kemmler G, Hummer M, Widschwendter C, et al. Dropout rates in placebo-controlled and active-control clinical trials of antipsychotic drugs: a meta-analysis. Arch Gen Psychiatry, 2005, 62(12): 1305-1312.
27.	Rabinowitz J, Levine SZ, Barkai O, et al. Dropout rates in randomized clinical trials of antipsychotics: a meta-analysis comparing first- and second-generation drugs and an examination of the role of trial design features. Schizophr Bull, 2009, 35(4): 775-788.
28.	Lurie I, Levine SZ. Meta-analysis of dropout rates in SSRIs versus placebo in randomized clinical trials of PTSD. J Nerv Ment Dis, 2010, 198(2): 116-124.

1. 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.
2. 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.
3. Irving E, van den Bor R, Welsing P, et al. Series: Pragmatic trials and real world evidence: Paper 7. Safety, quality and monitoring. J Clin Epidemiol, 2017, 91: 6-12.
4. Montori VM, Guyatt GH. Intention-to-treat principle. CMAJ, 2001, 165(10): 1339-1341.
5. O'Neill RT, Temple R. The prevention and treatment of missing data in clinical trials: an FDA perspective on the importance of dealing with it. Clin Pharmacol Ther, 2012, 91(3): 550-554.
6. Little RJ, D'Agostino R, Cohen ML, et al. The prevention and treatment of missing data in clinical trials. N Engl J Med, 2012, 367(14): 1355-1360.
7. 石清阳, 李玲, 李舍予, 等. 实效性随机对照试验中的统计方法(Ⅰ): 解决依从性问题. 中国循证医学杂志, 2021, 21(12): 1-8.
8. Pearl J. Causal diagrams for empirical research. Biometrika: Oxford University Press, 1995: 669-688.
9. Hernán MA, Hernández-Díaz S, Robins JM. A structural approach to selection bias. Epidemiology, 2004, 15(5): 615-625.
10. Greenland S. Response and follow-up bias in cohort studies. Am J Epidemiol, 1977, 106(3): 184-187.
11. Little RJ, Rubin DB. Statistical analysis with missing data. New York: John Wiley & Sons, 2019: 793.
12. Rubin DB. Inference and missing data. Biometrika: Oxford University Press, 1976: 581-592.
13. Knol MJ, Janssen KJ, Donders AR, et al. Unpredictable bias when using the missing indicator method or complete case analysis for missing confounder values: an empirical example. J Clin Epidemiol, 2010, 63(7): 728-736.
14. Robins JM, Finkelstein DM. Correcting for noncompliance and dependent censoring in an AIDS clinical trial with inverse probability of censoring weighted (IPCW) log-rank tests. Biometrics, 2000, 56(3): 779-788.
15. Hernán MA, Brumback B, Robins JM. Marginal structural models to estimate the joint causal effect of nonrandomized treatments. J Am Stat Assoc, 2001, 96(454): 440-448.
16. Saha C, Jones MP. Bias in the last observation carried forward method under informative dropout. J Stat Plan Inference, 2009, 139(2): 246-255.
17. Horvitz DG, Thompson DJ. A generalization of sampling without replacement from a finite universe. J Am stat Assoc, 1952, 47(260): 663-685.
18. Rubin DB. Multiple imputation after 18⁺ years. J Am stat Assoc, 1996, 91(434): 473-489.
19. Van Buuren S. Flexible imputation of missing data (second edition). Boca Raton, FL: CRC Press, 2018.
20. Herzog TN, Rubin DB. Using multiple imputations to handle nonresponse in sample surveys. New York: Academic Press, 1983: 209-245.
21. Royston P. Multiple imputation of missing values: further update of ice, with an emphasis on interval censoring. Stata J, 2007, 7(4): 445-464.
22. Von Hippel PT. How to impute interactions, squares, and other transformed variables. Sociol Method, 2009, 39(1): 265-291.
23. Van Buuren S, Karin GO. Multivariate imputation by chained equations in R. J Stat Soft, 2011, 45: i03.
24. Taylor JM, Cooper KL, Wei JT, et al. Use of multiple imputation to correct for nonresponse bias in a survey of urologic symptoms among African-American men. Am J Epidemiol, 2002, 156(8): 774-782.
25. Hsu CH, Taylor JM, Murray S, et al. Survival analysis using auxiliary variables via non-parametric multiple imputation. Stat Med, 2006, 25(20): 3503-3517.
26. Kemmler G, Hummer M, Widschwendter C, et al. Dropout rates in placebo-controlled and active-control clinical trials of antipsychotic drugs: a meta-analysis. Arch Gen Psychiatry, 2005, 62(12): 1305-1312.
27. Rabinowitz J, Levine SZ, Barkai O, et al. Dropout rates in randomized clinical trials of antipsychotics: a meta-analysis comparing first- and second-generation drugs and an examination of the role of trial design features. Schizophr Bull, 2009, 35(4): 775-788.
28. Lurie I, Levine SZ. Meta-analysis of dropout rates in SSRIs versus placebo in randomized clinical trials of PTSD. J Nerv Ment Dis, 2010, 198(2): 116-124.

Chinese Journal of Evidence-Based Medicine

Statistical methods in pragmatic randomized controlled trials (Ⅱ): Addressing missing outcome data

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