Comparison between SMART two-stage design and other two-stage designs commonly used in clinical trials_Chinese Journal of Evidence-Based Medicine

Authors：

ZHOU Sha ¹ , SHUAI Yuxing ² , JI Mengmeng ³ , QIAN Zhenzhen ⁴ , JIANG Yanhua ⁴ , JING Zhiwei ⁴ , LIU Chi ⁵ , LI Hongyi ⁵ , XUAN Guowei ⁵ ,  CHEN Xinlin ²

1. Second Affiliated Clinical School, Guangzhou University of Chinese Medicine, Guangzhou 510403, P. R. China;
2. Basic Medical College, Guangzhou University of Chinese Medicine, Guangzhou 510006, P. R. China;
3. School of Life Sciences, Ningxia University, Yinchuan 750021, P. R. China;
4. Institute of Clinical Basic Medicine, China Academy of Chinese Medical Sciences, Beijing 100700, P. R. China;
5. Guangdong Provincial Hospital of Traditional Chinese Medicine, Guangzhou 510120, P. R. China;

Corresponding author：

CHEN Xinlin, Email: chenxlsums@126.com

Keywords：

Clinical trial design; Adaptive design; Sequential multiple assignment randomized trial (SMART); Group sequential design; Cross-over design

DOI：

10.7507/1672-2531.202408007

Video：

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In recent years, investment in new drug development in China has surged; however, challenges such as difficulties in efficacy validation, high failure rates, and lengthy, costly clinical trials have been faced. The traditional model is insufficient for addressing these issues, necessitating innovation. Adaptive design (AD), particularly sequential multiple assignment randomized trials (SMART), has emerged as a flexible and efficient new pathway for drug development. This study focused on the two-stage design of SMART, analyzed its principles, and contrasted it with randomized controlled trials, group sequential designs, and crossover designs. The advantages of SMART are highlighted in terms of its precision in evaluating treatment strategies, minimizing sample waste, and enhancing the exploration of complex treatment pathways. Through case analyses, we demonstrated that SMART significantly improved clinical trial efficiency and the quality of treatment decisions, representing an innovative solution to the challenges of new drug development. This study aims to provide strategic references for clinical researchers and promote the adoption of adaptive designs in China, facilitating the efficient advancement of new drug development.

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6.	Murphy SA. An experimental design for the development of adaptive treatment strategies. Stat Med, 2005, 24(10): 1455-1481.
7.	Lavori PW, Dawson R, Rush AJ. Flexible treatment strategies in chronic disease: clinical and research implications. Biol Psychiatry, 2000, 48(6): 605-614.
8.	Oslin DW, Sayers S, Ross J, et al. Disease management for depression and at-risk drinking via telephone in an older population of veterans. Psychosom Med, 2003, 65(6): 931-937.
9.	Murphy SA. Optimal dynamic treatment regimes. J R Stat Soc, 2003, 65(2): 331-366.
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14.	罗辉, 刘建平. 从系统综述看中国随机对照试验的质量. 中西医结合学报, 2011, 9(7): 697-701.
15.	Nahum-Shani I, Qian M, Almirall D, et al. Experimental design and primary data analysis methods for comparing adaptive interventions. Psychol Methods, 2012, 17(4): 457-477.
16.	Cook TD, Campbell DT. Quasi-experimentation: design & analysis issue for field settings. Rand McNally College Pub. Co. , 1979.
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18.	Corrao G, Cantarutti A. Building reliable evidence from real-world data: needs, methods, cautiousness and recommendations. Pulm Pharmacol Ther, 2018, 53: 61-67.
19.	Fang F, Hochstedler KA, Tamura RN, et al. Bayesian methods to compare dose levels with placebo in a small n, sequential, multiple assignment, randomized trial. Stat Med, 2021, 40(4): 963-977.
20.	Gunter L, Zhu J, Murphy S. Variable selection for qualitative interactions in personalized medicine while controlling the family-wise error rate. J Biopharm Stat, 2011, 21(6): 1063-1078.
21.	张文彤, 李晓松, 倪宗瓒. 交叉设计中顺序效应的影响及其分析方法. 数理医药学杂志, 1999, (4): 305-306.
22.	Ruppert AS, Yin J, Davidian M, et al. Application of a sequential multiple assignment randomized trial (SMART) design in older patients with chronic lymphocytic leukemia. Ann Oncol, 2019, 30(4): 542-550.
23.	Burger JA, Tedeschi A, Barr PM, et al. Ibrutinib as initial therapy for patients with chronic lymphocytic leukemia. N Engl J Med, 2015, 373(25): 2425-2437.
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26.	Nahum-Shani I, Ertefaie A, Lu XL, et al. A SMART data analysis method for constructing adaptive treatment strategies for substance use disorders. Addiction, 2017, 112(5): 901-909.
27.	Lu X, Shan G. Two-stage response adaptive randomization designs for multi-arm trials with binary outcome. J Biopharm Stat, 2023, 34(4): 526-538.
28.	Lunceford JK, Davidian M, Tsiatis AA. Estimation of survival distributions of treatment policies in two-stage randomization designs in clinical trials. Biometrics, 2002, 58(1): 48-57.
29.	U. S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER), Center for Biologics Evaluation and Research (CBER). Adaptive designs for clinical trials of drugs and biologics guidance for industry. Biostatistics, 2018.
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31.	国家药品监督管理局药品审评中心. 药物临床试验适应性设计指导原则(试行). 2021.
32.	孙源, 鞠传兰, 郑薇, 等. 中医药序列多重分配随机试验设计分析思路及统计方法应用. 现代中医临床, 2024, 31(5): 86-91.
33.	Artman WJ, Nahum-Shani I, Wu T, et al. Power analysis in a SMART design: sample size estimation for determining the best embedded dynamic treatment regime. Biostatistics, 2020, 21(3): 432-448.
34.	卜志军, 张雨欢, 孙源, 等. 序贯多分配随机试验设计样本量估算方法及应用. 现代中医临床, 2024, 31(4): 39-43.
35.	周莎. 金匮泽泻汤辨治痰湿型良性阵发性位置性眩晕的SMART设计探索研究. 北京: 中国中医科学院, 2022.
36.	于亚南, 荆志伟, 刘骏, 等. 适应性治疗临床试验设计与辨证论治疗效评价的思考. 中国中医基础医学杂志, 2012, 18(3): 316-318.
37.	曹卉娟, 刘建平. SMART设计在中医非药物疗法疗效评价中的应用探讨. 北京中医药, 2020, 39(1): 78-80.

1. Chow SC, Chang M. Adaptive design methods in clinical trials - a review. Orphanet J Rare Dis, 2008, 3: 11.
2. 韩双伦. 早期临床试验适应性设计的方法探讨. 哈尔滨: 黑龙江大学, 2020.
3. Food and Drug Administration. Adaptive design clinical trials for drugs and biologies guidance for industry. 2019.
4. Pignatti F, Aronsson B, Vamvakas S, et al. Clinical trials for registration in the European Union: the EMEA 5-year experience in oncology. Crit Rev Oncol Hematol, 2002, 42(2): 123-135.
5. Lei H, Nahum-Shani I, Lynch K, et al. A "SMART" design for building individualized treatment sequences. Annu Rev Clin Psychol, 2012, 8: 21-48.
6. Murphy SA. An experimental design for the development of adaptive treatment strategies. Stat Med, 2005, 24(10): 1455-1481.
7. Lavori PW, Dawson R, Rush AJ. Flexible treatment strategies in chronic disease: clinical and research implications. Biol Psychiatry, 2000, 48(6): 605-614.
8. Oslin DW, Sayers S, Ross J, et al. Disease management for depression and at-risk drinking via telephone in an older population of veterans. Psychosom Med, 2003, 65(6): 931-937.
9. Murphy SA. Optimal dynamic treatment regimes. J R Stat Soc, 2003, 65(2): 331-366.
10. Moodie EE, Richardson TS, Stephens DA. Demystifying optimal dynamic treatment regimes. Biometrics, 2007, 63(2): 447-455.
11. Wahed AS, Tsiatis AA. Optimal estimator for the survival distribution and related quantities for treatment policies in two-stage randomization designs in clinical trials. Biometrics, 2004, 60(1): 124-133.
12. Collins LM, Murphy SA, Bierman KL. A conceptual framework for adaptive preventive interventions. Prev Sci, 2004, 5(3): 185-196.
13. Mober D, Hopewell S, Schuijz KF, et a1. CONSORT 2010 explanation and elaboration: updated guide - lines for reporting parallel group randomised trials. Int J Surg, 2012, 10(1): 28-55.
14. 罗辉, 刘建平. 从系统综述看中国随机对照试验的质量. 中西医结合学报, 2011, 9(7): 697-701.
15. Nahum-Shani I, Qian M, Almirall D, et al. Experimental design and primary data analysis methods for comparing adaptive interventions. Psychol Methods, 2012, 17(4): 457-477.
16. Cook TD, Campbell DT. Quasi-experimentation: design & analysis issue for field settings. Rand McNally College Pub. Co. , 1979.
17. Shadish WR. Revisiting field experimentation: field notes for the future. Psychol Methods, 2002, 7(1): 3-18.
18. Corrao G, Cantarutti A. Building reliable evidence from real-world data: needs, methods, cautiousness and recommendations. Pulm Pharmacol Ther, 2018, 53: 61-67.
19. Fang F, Hochstedler KA, Tamura RN, et al. Bayesian methods to compare dose levels with placebo in a small n, sequential, multiple assignment, randomized trial. Stat Med, 2021, 40(4): 963-977.
20. Gunter L, Zhu J, Murphy S. Variable selection for qualitative interactions in personalized medicine while controlling the family-wise error rate. J Biopharm Stat, 2011, 21(6): 1063-1078.
21. 张文彤, 李晓松, 倪宗瓒. 交叉设计中顺序效应的影响及其分析方法. 数理医药学杂志, 1999, (4): 305-306.
22. Ruppert AS, Yin J, Davidian M, et al. Application of a sequential multiple assignment randomized trial (SMART) design in older patients with chronic lymphocytic leukemia. Ann Oncol, 2019, 30(4): 542-550.
23. Burger JA, Tedeschi A, Barr PM, et al. Ibrutinib as initial therapy for patients with chronic lymphocytic leukemia. N Engl J Med, 2015, 373(25): 2425-2437.
24. Byrd JC, Furman RR, Coutre SE, et al. Targeting BTK with ibrutinib in relapsed chronic lymphocytic leukemia. N Engl J Med, 2013, 369(1): 32-42.
25. Wolbers M, Helterbrand JD. Two-stage randomization designs in drug development. Stat Med, 2008, 27(21): 4161-4174.
26. Nahum-Shani I, Ertefaie A, Lu XL, et al. A SMART data analysis method for constructing adaptive treatment strategies for substance use disorders. Addiction, 2017, 112(5): 901-909.
27. Lu X, Shan G. Two-stage response adaptive randomization designs for multi-arm trials with binary outcome. J Biopharm Stat, 2023, 34(4): 526-538.
28. Lunceford JK, Davidian M, Tsiatis AA. Estimation of survival distributions of treatment policies in two-stage randomization designs in clinical trials. Biometrics, 2002, 58(1): 48-57.
29. U. S. Department of Health and Human Services, Food and Drug Administration, Center for Drug Evaluation and Research (CDER), Center for Biologics Evaluation and Research (CBER). Adaptive designs for clinical trials of drugs and biologics guidance for industry. Biostatistics, 2018.
30. Kahn J. FDA in brief: FDA launches new pilot to advance innovative clinical trial designs as part of agency’s broader program to modernize drug development and promote innovation in drugs targeted to unmet needs. 2022.
31. 国家药品监督管理局药品审评中心. 药物临床试验适应性设计指导原则(试行). 2021.
32. 孙源, 鞠传兰, 郑薇, 等. 中医药序列多重分配随机试验设计分析思路及统计方法应用. 现代中医临床, 2024, 31(5): 86-91.
33. Artman WJ, Nahum-Shani I, Wu T, et al. Power analysis in a SMART design: sample size estimation for determining the best embedded dynamic treatment regime. Biostatistics, 2020, 21(3): 432-448.
34. 卜志军, 张雨欢, 孙源, 等. 序贯多分配随机试验设计样本量估算方法及应用. 现代中医临床, 2024, 31(4): 39-43.
35. 周莎. 金匮泽泻汤辨治痰湿型良性阵发性位置性眩晕的SMART设计探索研究. 北京: 中国中医科学院, 2022.
36. 于亚南, 荆志伟, 刘骏, 等. 适应性治疗临床试验设计与辨证论治疗效评价的思考. 中国中医基础医学杂志, 2012, 18(3): 316-318.
37. 曹卉娟, 刘建平. SMART设计在中医非药物疗法疗效评价中的应用探讨. 北京中医药, 2020, 39(1): 78-80.

Chinese Journal of Evidence-Based Medicine

Latest ArticlesComparison between SMART two-stage design and other two-stage designs commonly used in clinical trials

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