Method and application of sample size re-estimation in adaptive design_Chinese Journal of Evidence-Based Medicine

Authors：

CHEN Zhuoqun ¹ , SHUAI Wei ² , GU Jing ³ , LAI Yingsi ³ , LIU Yu ⁴ , XU Qian ⁵ , ZHANG Junguo ⁵ , HOU Zhengkun ⁶ ,  CHEN Xinlin ⁵

1. Department of Educational Affairs, Guangzhou University of Chinese Medicine, Guangzhou 510006, P. R. China;
2. The Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou 510006, P. R. China;
3. Department of Medical statistics, School of Public Health, Sun Yat-sen University, Guangzhou 510080, P. R. China;
4. School of Public Health and Management, Guangzhou University of Chinese Medicine, Guangzhou 510006, P. R. China;
5. School of Basic Medical Sciences, Guangzhou University of Chinese Medicine, Guangzhou 510006, P. R. China;
6. The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 510405, P. R. China;

Corresponding author：

CHEN Xinlin, Email: chenxlsums@126.com

Keywords：

Sample size re-estimation; Adaptive design; Interim analysis

DOI：

10.7507/1672-2531.202411181

Video：

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Sample size re-estimation (SSR) refers to the recalculation of the sample size using the existing trial data as original planned to ensure that the final statistical test achieved the pre-defined goals. SSR can enhance research efficiency, save trial costs, and accelerate the research process. Depending on whether the group assignment of the patients is known, SSR is divided into blinded sample size re-estimation and unblinded sample size re-estimation. Blinded sample size re-estimation can estimate the variance of the primary evaluation index through the EM algorithm or single sample variance re-estimation method, and then calculate the sample size. Unblinded sample size re-estimation can calculate the sample size by estimating the overall variance or therapeutic effect difference, but it needs to control the family wise type I error (FWER) rate. Cui-Hung-Wang method, conditional rejection probability method, P-value combination method, conditional error function, and promising zone are common methods used to control FWER. Currently, there are application examples of SSR methods. With the maturation of related theories and the popularization of methods, it is expected to be widely applied in clinical trials, especially in traditional Chinese medicine clinical trials in the future.

1.	杜培艳, 于亚南, 刘骏, 等. 基于FDA新版指南草案浅谈适应性设计的发展. 药物流行病学杂志, 2019, 28(9): 613-619,628.
2.	肖意可, 胡飞芳, 刘中强, 等. 适应性设计在新药临床试验中的应用. 中国新药杂志, 2017, 26(4): 389-393.
3.	国家药品监督管理局药品审评中心. 关于《药物临床试验样本量估计指导原则(征求意见稿)》公开征求意见的通知. 2023.
4.	陈垂雄. 希望区域法在适应性设计临床试验中的应用研究. 西安: 中国人民解放军空军军医大学, 2024.
5.	陈建平. 临床试验期中分析与决策: 条件把握度的应用. 南京: 南京医科大学, 2011.
6.	戴启刚. 适应性设计中样本量调整方法的评价. 南京: 南京医科大学, 2010.
7.	毛雨彤. 国际多区域临床试验目标区域样本量适应性调整策略研究. 桂林: 桂林电子科技大学, 2022.
8.	王素珍. 自适应设计中样本量调整问题的研究及Web实现. 西安: 第四军医大学, 2008.
9.	徐文静. 适应性设计中不同α消耗策略的比较研究. 桂林: 桂林电子科技大学, 2021.
10.	于莉莉, 薛富波, 王素珍, 等. 临床试验中自适应设计的样本量再估计方法. 中国卫生统计, 2008, 25(6): 657-660.
11.	Lawrence GA, Shih WJ. Sample size re-estimation without unblinding for normally distributed outcomes with unknown variance. Commun Stat Theory Methods, 1992, 21(10): 2833-2853.
12.	Wittes J, Brittain E. The role of internal pilot studies in increasing the efficiency of clinical trials. Stat Med, 1990, 9(1-2): 65-71.
13.	蒋志伟. 临床试验中成组序贯设计的关键技术探讨. 西安: 第四军医大学, 2013.
14.	Cui L, Hung HM, Wang SJ. Modification of sample size in group sequential clinical trials. Biometrics, 1999, 55(3): 853-857.
15.	Li Q, Lin J, Lin Y. Adaptive design implementation in confirmatory trials: methods, practical considerations and case studies. Contemp Clin Trials, 2020, 98: 106096.
16.	Müller HH, Schäfer H. Adaptive group sequential designs for clinical trials: combining the advantages of adaptive and of classical group sequential approaches. Biometrics, 2001, 57(3): 886-891.
17.	陈思敏, 吴海燕, 傅利强, 等. P值合并法中基于有效终止概率的适应性样本量调整的优化研究. 中国卫生统计, 2023, 40(2): 178-182.
18.	Chang M. Adaptive design method based on sum of p-values. Stat Med, 2007, 26(14): 2772-2784.
19.	Lehmacher W, Wassmer G. Adaptive sample size calculations in group sequential trials. Biometrics, 1999, 55(4): 1286-1290.
20.	Bauer P, Köhne K. Evaluation of experiments with adaptive interim analyses. Biometrics, 1994, 50(4): 1029-1041.
21.	Edwards JM, Walters SJ, Kunz C, et al. A systematic review of the "promising zone" design. Trials, 2020, 21(1): 1000.
22.	陈垂雄, 王文文, 黄曼丽, 等. 基于期中条件检验效能的希望区域法在临床试验非盲样本量重估中的应用. 空军军医大学学报, 2024.
23.	程建成, 柏建岭, 黄丽红, 等. 一种基于条件把握度法和EM算法相结合的样本量再估计. 中国卫生统计, 2018, 35(1): 33-37,42.
24.	Zhang J, Saju C. A systematic review of randomised controlled trials with adaptive and traditional group sequential designs - applications in cardiovascular clinical trials. BMC Med Res Methodol, 2023, 23(1): 200.
25.	Torres VE, Meijer E, Bae KT, et al. Rationale and design of the TEMPO (Tolvaptan efficacy and safety in management of autosomal dominant polycystic kidney disease and its outcomes) 3-4 study. Am J Kidney Dis, 2011, 57(5): 692-699.
26.	李慧, 符文彬, 梁伟雄, 等. 基于α消耗函数法的针灸治疗颈型颈椎病临床试验期中分析. 中华中医药学刊, 2017, 35(2): 394-397.
27.	Miller E, Gallo P, He W, et al. DIA's Adaptive Design Scientific Working Group (ADSWG): best practices case studies for "Less Well-understood" adaptive designs. Ther Innov Regul Sci, 2017, 51(1): 77-88.
28.	Grayling MJ, Wheeler GM. A review of available software for adaptive clinical trial design. Clin Trials, 2020, 17(3): 323-331.
29.	Ciolino JD, Kaizer AM, Bonner LB. Guidance on interim analysis methods in clinical trials. J Clin Transl Sci, 2023, 7(1): e124.

1. 杜培艳, 于亚南, 刘骏, 等. 基于FDA新版指南草案浅谈适应性设计的发展. 药物流行病学杂志, 2019, 28(9): 613-619,628.
2. 肖意可, 胡飞芳, 刘中强, 等. 适应性设计在新药临床试验中的应用. 中国新药杂志, 2017, 26(4): 389-393.
3. 国家药品监督管理局药品审评中心. 关于《药物临床试验样本量估计指导原则(征求意见稿)》公开征求意见的通知. 2023.
4. 陈垂雄. 希望区域法在适应性设计临床试验中的应用研究. 西安: 中国人民解放军空军军医大学, 2024.
5. 陈建平. 临床试验期中分析与决策: 条件把握度的应用. 南京: 南京医科大学, 2011.
6. 戴启刚. 适应性设计中样本量调整方法的评价. 南京: 南京医科大学, 2010.
7. 毛雨彤. 国际多区域临床试验目标区域样本量适应性调整策略研究. 桂林: 桂林电子科技大学, 2022.
8. 王素珍. 自适应设计中样本量调整问题的研究及Web实现. 西安: 第四军医大学, 2008.
9. 徐文静. 适应性设计中不同α消耗策略的比较研究. 桂林: 桂林电子科技大学, 2021.
10. 于莉莉, 薛富波, 王素珍, 等. 临床试验中自适应设计的样本量再估计方法. 中国卫生统计, 2008, 25(6): 657-660.
11. Lawrence GA, Shih WJ. Sample size re-estimation without unblinding for normally distributed outcomes with unknown variance. Commun Stat Theory Methods, 1992, 21(10): 2833-2853.
12. Wittes J, Brittain E. The role of internal pilot studies in increasing the efficiency of clinical trials. Stat Med, 1990, 9(1-2): 65-71.
13. 蒋志伟. 临床试验中成组序贯设计的关键技术探讨. 西安: 第四军医大学, 2013.
14. Cui L, Hung HM, Wang SJ. Modification of sample size in group sequential clinical trials. Biometrics, 1999, 55(3): 853-857.
15. Li Q, Lin J, Lin Y. Adaptive design implementation in confirmatory trials: methods, practical considerations and case studies. Contemp Clin Trials, 2020, 98: 106096.
16. Müller HH, Schäfer H. Adaptive group sequential designs for clinical trials: combining the advantages of adaptive and of classical group sequential approaches. Biometrics, 2001, 57(3): 886-891.
17. 陈思敏, 吴海燕, 傅利强, 等. P值合并法中基于有效终止概率的适应性样本量调整的优化研究. 中国卫生统计, 2023, 40(2): 178-182.
18. Chang M. Adaptive design method based on sum of p-values. Stat Med, 2007, 26(14): 2772-2784.
19. Lehmacher W, Wassmer G. Adaptive sample size calculations in group sequential trials. Biometrics, 1999, 55(4): 1286-1290.
20. Bauer P, Köhne K. Evaluation of experiments with adaptive interim analyses. Biometrics, 1994, 50(4): 1029-1041.
21. Edwards JM, Walters SJ, Kunz C, et al. A systematic review of the "promising zone" design. Trials, 2020, 21(1): 1000.
22. 陈垂雄, 王文文, 黄曼丽, 等. 基于期中条件检验效能的希望区域法在临床试验非盲样本量重估中的应用. 空军军医大学学报, 2024.
23. 程建成, 柏建岭, 黄丽红, 等. 一种基于条件把握度法和EM算法相结合的样本量再估计. 中国卫生统计, 2018, 35(1): 33-37,42.
24. Zhang J, Saju C. A systematic review of randomised controlled trials with adaptive and traditional group sequential designs - applications in cardiovascular clinical trials. BMC Med Res Methodol, 2023, 23(1): 200.
25. Torres VE, Meijer E, Bae KT, et al. Rationale and design of the TEMPO (Tolvaptan efficacy and safety in management of autosomal dominant polycystic kidney disease and its outcomes) 3-4 study. Am J Kidney Dis, 2011, 57(5): 692-699.
26. 李慧, 符文彬, 梁伟雄, 等. 基于α消耗函数法的针灸治疗颈型颈椎病临床试验期中分析. 中华中医药学刊, 2017, 35(2): 394-397.
27. Miller E, Gallo P, He W, et al. DIA's Adaptive Design Scientific Working Group (ADSWG): best practices case studies for "Less Well-understood" adaptive designs. Ther Innov Regul Sci, 2017, 51(1): 77-88.
28. Grayling MJ, Wheeler GM. A review of available software for adaptive clinical trial design. Clin Trials, 2020, 17(3): 323-331.
29. Ciolino JD, Kaizer AM, Bonner LB. Guidance on interim analysis methods in clinical trials. J Clin Transl Sci, 2023, 7(1): e124.

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