Covariate-adaptive randomization and restricted randomization: R language implementation_Chinese Journal of Evidence-Based Medicine

Authors：

SHUAI Yuxing1 ¹ , SHEN Xuewei ² , WANG Liangjing ³ , ZHOU Sha ³ , ZHU Ke1 ¹ , GU Jing ⁴ , CHEN Zhuoqun ¹ , LIU Yu ² ,  CHEN Xinlin1 ¹

1. School of Basic Medical Science, Guangzhou University of Chinese Medicine, Guangzhou 510006, P. R. China;
2. School of Public Health and Management, Guangzhou University of Chinese Medicine, Guangzhou 510006, P. R. China;
3. The Second Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou 510006, P. R. China;
4. Shool of Public Health, Sun Yat-sen University, Guangzhou 510275, P. R. China;

Corresponding?author：

CHEN Xinlin1, Email: chenxlsums@126.com

Keywords：

Covariate-adaptive randomization; Restricted randomization; R language; Minimization method; Efron's biased coin design; Clinical trials

DOI：

10.7507/1672-2531.202503069

Video：

Export PDF Favorites Scan Get Citation

Abstract Full text Figures/Tables Video References Cited by

Objective To introduce the theoretical foundation, application scenarios, and implementation in R language of covariate-adaptive randomization (CAR) and restricted randomization (RR). Methods This article initially expounds the significance of CAR and RR in clinical trials, particularly in balancing covariates between treatment groups on the basis of dynamic adjustment and pre-defined rules, in order to enhance the accuracy and reliability of trial outcomes. Results RR is applicable to large-scale trials, ensuring balance between groups but potentially inducing selection bias; CAR is suitable for small-sample and complex covariate trials, improving accuracy yet having complex implementation. In clinical trials of traditional Chinese medicine, CAR enables personalized group allocation, and RR ensures baseline balance. Dynamic randomization strategies enhance the flexibility of trials. Conclusion Through code examples in R language, this study offers practical guidance for researchers to implement these randomization methods, ensuring the scientificity and rigor of data processing and analysis.

1.	Marchenko O, Nolan C. Implementing adaptive designs: operational considerations, putting it all together//He W, Pinheiro J, Kuznetsova OM. Practical considerations for adaptive trial design and implementation. Springer: New York. 2014.
2.	關于發布中藥新藥臨床研究一般原則等4個技術指導原則的通告國家食品藥品監督管理總局 2015年第83號. 中國制藥信息, 2016, 32(4): 80.
3.	Higham R, Tharmanathan P, Birks Y. Use and reporting of restricted randomization: a review. J Eval Clin Pract, 2015, 21(6): 1205-1211.
4.	Robertson D, Lee K, López-Kolkovska B, et al. Response-adaptive randomization in clinical trials: from myths to practical considerations1. Stat Sci, 2023, 38(2): 185-208.
5.	Bugni F, Canay I, Shaikh A. Inference under covariate-adaptive randomization. J Am Stat Assoc, 2018, 113(524): 1784-1796.
6.	高穎. 中醫辨證治療非糜爛性胃食管反流病46例臨床觀察. 實用中西醫結合臨床, 2010, 10(6): 25-26.
7.	Shan G, Li Y, Lu X, et al. Comparison of pocock and simon's covariate-adaptive randomization procedures in clinical trials. BMC Med Res Methodol, 2024, 24(1): 22.
8.	Taves D. Minimization - new method of assigning patients to treatment and control groups. Clin Pharmacol Ther, 1974, 15(5): 443-453.
9.	Pocock S, Simon R. Sequential treatment assignment with balancing for prognostic factors in controlled clinical trial. Biometrics, 1975, 31(1): 103-115.
10.	Zagoraiou M. Choosing a covariate-adaptive randomization procedure in practice. J Biopharm Stat, 2017, 27(5): 845-857.
11.	Lin Y, Zhu M, Su Z. The pursuit of balance: an overview of covariate-adaptive randomization techniques in clinical trials. Contemp Clin Trials, 2015, 45: 21-25.
12.	Rahman N, Maskell N, West A, et al. Intrapleural use of tissue plasminogen activator and DNase in pleural infection. N Engl J Med, 2011, 365(6): 518-526.
13.	Hua W, Randall B, John C M, et al. Validity and power of minimization algorithm in longitudinal analysis of clinical trials. Biostat Epidemiol, 2017, 1(1): 59-77.
14.	Shan G, Li Y, Lu X, et al. Comparison of Pocock and Simon's covariate-adaptive randomization procedures in clinical trials. BMC Med Res Methodol, 2024, 24(1): 22.
15.	Wang T, Ma W. The impact of misclassification on covariate-adaptive randomized clinical trials. Biometrics, 2021, 77(2): 451-464.
16.	Ciolino J, Palac H, Yang A, et al. Ideal vs. real: a systematic review on handling covariates in randomized controlled trials. BMC Med Res Methodol, 2019, 19(1): 136.
17.	Rosenberger W, Sverdlov O, Hu F. Adaptive randomization for clinical trials. J Biopharm Stat, 2012, 22(4): 719-736.
18.	YU FF. Principles of clinical trials: bias and precision control//Piantadosi S, Meinert CL. Principles and practice of clinical trials. Cham; Springer International Publishing. 2022.
19.	Johnson V. Conditioning Efron's biased coin design to ensure final balance. Electron J Stat, 2021, 15(2): 4027-4060.
20.	Chen YP. Which design is better. Ehrenfest urn versus biased coin. Adv Appl Probab, 2000, 32(3): 738-749.
21.	Barton BA. Controlling bias in randomized clinical trials//Piantadosi S, Meinert CL. Principles and practice of clinical trials. Cham: Springer International Publishing, 2022.
22.	Ivers N, Halperin I, Barnsley J, et al. Allocation techniques for balance at baseline in cluster randomized trials: a methodological review. Trials, 2012, 13(1): 120.
23.	Chabouis H, Chabouis F, Gillaizeau F, et al. Randomization in clinical trials: stratification or minimization. The HERMES free simulation software. Clin Oral Investig, 2014, 18(1): 25-34.
24.	Sajobi T, Singh G, Lowerison M, et al. Minimal sufficient balance randomization for sequential randomized controlled trial designs: results from the ESCAPE trial. Trials, 2017, 18(1): 516.
25.	John E, Abrams K, Brightling C, et al. Assessing causal treatment effect estimation when using large observational datasets. BMC Med Res Methodol, 2019, 19(1): 207.
26.	McPherson G, Campbell M, Elbourne D. Investigating the relationship between predictability and imbalance in minimisation: a simulation study. Trials, 2013, 14(1): 86.
27.	Holmberg M, Andersen L. Adjustment for baseline characteristics in randomized clinical trials. JAMA, 2022, 328(21): 2155-2156.
28.	Sverdlov O, Ryeznik Y, Anisimov V, et al. Selecting a randomization method for a multi-center clinical trial with stochastic recruitment considerations. BMC Med Res Methodol, 2024, 24(1): 52.

1. Marchenko O, Nolan C. Implementing adaptive designs: operational considerations, putting it all together//He W, Pinheiro J, Kuznetsova OM. Practical considerations for adaptive trial design and implementation. Springer: New York. 2014.
2. 關于發布中藥新藥臨床研究一般原則等4個技術指導原則的通告國家食品藥品監督管理總局 2015年第83號. 中國制藥信息, 2016, 32(4): 80.
3. Higham R, Tharmanathan P, Birks Y. Use and reporting of restricted randomization: a review. J Eval Clin Pract, 2015, 21(6): 1205-1211.
4. Robertson D, Lee K, López-Kolkovska B, et al. Response-adaptive randomization in clinical trials: from myths to practical considerations1. Stat Sci, 2023, 38(2): 185-208.
5. Bugni F, Canay I, Shaikh A. Inference under covariate-adaptive randomization. J Am Stat Assoc, 2018, 113(524): 1784-1796.
6. 高穎. 中醫辨證治療非糜爛性胃食管反流病46例臨床觀察. 實用中西醫結合臨床, 2010, 10(6): 25-26.
7. Shan G, Li Y, Lu X, et al. Comparison of pocock and simon's covariate-adaptive randomization procedures in clinical trials. BMC Med Res Methodol, 2024, 24(1): 22.
8. Taves D. Minimization - new method of assigning patients to treatment and control groups. Clin Pharmacol Ther, 1974, 15(5): 443-453.
9. Pocock S, Simon R. Sequential treatment assignment with balancing for prognostic factors in controlled clinical trial. Biometrics, 1975, 31(1): 103-115.
10. Zagoraiou M. Choosing a covariate-adaptive randomization procedure in practice. J Biopharm Stat, 2017, 27(5): 845-857.
11. Lin Y, Zhu M, Su Z. The pursuit of balance: an overview of covariate-adaptive randomization techniques in clinical trials. Contemp Clin Trials, 2015, 45: 21-25.
12. Rahman N, Maskell N, West A, et al. Intrapleural use of tissue plasminogen activator and DNase in pleural infection. N Engl J Med, 2011, 365(6): 518-526.
13. Hua W, Randall B, John C M, et al. Validity and power of minimization algorithm in longitudinal analysis of clinical trials. Biostat Epidemiol, 2017, 1(1): 59-77.
14. Shan G, Li Y, Lu X, et al. Comparison of Pocock and Simon's covariate-adaptive randomization procedures in clinical trials. BMC Med Res Methodol, 2024, 24(1): 22.
15. Wang T, Ma W. The impact of misclassification on covariate-adaptive randomized clinical trials. Biometrics, 2021, 77(2): 451-464.
16. Ciolino J, Palac H, Yang A, et al. Ideal vs. real: a systematic review on handling covariates in randomized controlled trials. BMC Med Res Methodol, 2019, 19(1): 136.
17. Rosenberger W, Sverdlov O, Hu F. Adaptive randomization for clinical trials. J Biopharm Stat, 2012, 22(4): 719-736.
18. YU FF. Principles of clinical trials: bias and precision control//Piantadosi S, Meinert CL. Principles and practice of clinical trials. Cham; Springer International Publishing. 2022.
19. Johnson V. Conditioning Efron's biased coin design to ensure final balance. Electron J Stat, 2021, 15(2): 4027-4060.
20. Chen YP. Which design is better. Ehrenfest urn versus biased coin. Adv Appl Probab, 2000, 32(3): 738-749.
21. Barton BA. Controlling bias in randomized clinical trials//Piantadosi S, Meinert CL. Principles and practice of clinical trials. Cham: Springer International Publishing, 2022.
22. Ivers N, Halperin I, Barnsley J, et al. Allocation techniques for balance at baseline in cluster randomized trials: a methodological review. Trials, 2012, 13(1): 120.
23. Chabouis H, Chabouis F, Gillaizeau F, et al. Randomization in clinical trials: stratification or minimization. The HERMES free simulation software. Clin Oral Investig, 2014, 18(1): 25-34.
24. Sajobi T, Singh G, Lowerison M, et al. Minimal sufficient balance randomization for sequential randomized controlled trial designs: results from the ESCAPE trial. Trials, 2017, 18(1): 516.
25. John E, Abrams K, Brightling C, et al. Assessing causal treatment effect estimation when using large observational datasets. BMC Med Res Methodol, 2019, 19(1): 207.
26. McPherson G, Campbell M, Elbourne D. Investigating the relationship between predictability and imbalance in minimisation: a simulation study. Trials, 2013, 14(1): 86.
27. Holmberg M, Andersen L. Adjustment for baseline characteristics in randomized clinical trials. JAMA, 2022, 328(21): 2155-2156.
28. Sverdlov O, Ryeznik Y, Anisimov V, et al. Selecting a randomization method for a multi-center clinical trial with stochastic recruitment considerations. BMC Med Res Methodol, 2024, 24(1): 52.

Chinese Journal of Evidence-Based Medicine

Latest ArticlesCovariate-adaptive randomization and restricted randomization: R language implementation

Abstract Full text Figures/Tables Video References Cited by

Format

Content