Common methods and software implementation for initial sample size estimation in adaptive designs_Chinese Journal of Evidence-Based Medicine

Authors：

XIONG Yaxin ^1,2 , LIU Jun ^1,3 , YANG Xiaoguang ¹ , SHU Lin ¹ , ZHOU Pengfei ⁴ ,  LI Yunming ^1,2,3

1. Office of Medical Information and Data, Medical Support Center, The General Hospital of Western Theater Command, PLA, Chengdu 610083, P. R. China;
2. School of Public Health, North Sichuan Medical College, Nanchong 637000, P. R. China;
3. Department of Statistics, College of Mathematics, Southwest Jiaotong University, Chengdu 610031, P. R. China;
4. Department of Health Economics, The General Hospital of Western Theater Command, PLA, Chengdu 610083, P. R. China;

Corresponding?author：

LI Yunming, Email: lee3082@sina.com

Keywords：

Adaptive design; Sample size estimation; Group sequential design

DOI：

10.7507/1672-2531.202511070

Video：

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As a prospective and dynamic research paradigm, adaptive design allows sample size, endpoints, or recruitment strategies to be modified based on predefined rules during the course of a clinical trial, thereby improving power and ethical soundness while controlling the type I errors. However, the estimation of initial sample size, which is the core of the paradigm, still faces challenges such as methodological complexity, high dependence on specialized software, and limited localized application. Therefore, this paper systematically reviews the methodological progress in initial sample size estimation for adaptive designs, covering various variable types including continuous, binary, survival, and ordinal data. We summarize key parameters of commonly used strategies, particularly group sequential designs, such as alpha spending functions, information time, and sample size inflation factors, and compare the implementation approaches and applicability in mainstream statistical software, including PASS, SAS, Stata and R. This paper briefly describes the initial sample size estimation methods used for different variable types in adaptive designs so as to provide references for clinical research designers and implementers.

Citation： XIONG Yaxin, LIU Jun, YANG Xiaoguang, SHU Lin, ZHOU Pengfei, LI Yunming. Common methods and software implementation for initial sample size estimation in adaptive designs. Chinese Journal of Evidence-Based Medicine, 2026, 26(7): 861-868. doi: 10.7507/1672-2531.202511070 Copy

1.	黃曼麗, 李晨, 葛偉, 等. 臨床研究樣本代表性評估方法的對比研究. 中國衛生統計, 2024, 41(2): 167-174.
2.	潘岳松, 金奧銘, 王夢星. 臨床研究樣本量的估計方法和常見錯誤. 中國卒中雜志, 2022, 17(1): 31-35.
3.	國家藥品監督管理局藥品審評中心. 國家藥監局藥審中心關于發布《藥物臨床試驗適應性設計指導原則(試行)》的通告(2021年第6號). 2021.
4.	石巖巖, 張華, 趙一鳴. 臨床研究樣本量估計的參數選擇. 中華兒科雜志, 2024, 62(4): 350-352.
5.	王雨寧, 徐暢, 鄧可, 等. 適應性設計在臨床試驗中的應用. 中國循證醫學雜志, 2020, 20(4): 487-491.
6.	Pallmann P, Bedding AW, Choodari-Oskooei B, et al. Adaptive designs in clinical trials: why use them, and how to run and report them. BMC Med, 2018, 16(1): 29.
7.	U. S. Food and Drug Administration. Adaptive design clinical trials for drugs and biologics: guidance for industry. 2019.
8.	陳平雁. 臨床試驗中樣本量確定的統計學考慮. 中國衛生統計, 2015, 32(4): 727-731, 733.
9.	曾于珍, 陳世耀. 臨床研究結局指標選擇與樣本量估計. 協和醫學雜志, 2018, 9(1): 87-92.
10.	Grayling MJ, Wheeler GM. A review of available software for adaptive clinical trial design. Clin Trials, 2020, 17(3): 323-331.
11.	林潔, 孫志明. SAS、PASS、Stata三種常用軟件樣本量計算方法及結果差異的比較. 中國醫藥導報, 2015, 12(18): 133-137.
12.	Cui L, Hung HM, Wang SJ. Modification of sample size in group sequential clinical trials. Biometrics, 1999, 55(3): 853-857.
13.	傅利強, 陳思敏, 吳海燕, 等. 成組序貫設計中基于實際信息量的臨界值校正方法的比較研究. 中國衛生統計, 2022, 39(4): 542-544, 549.
14.	Kilbourne A, Chinman M, Rogal S, et al. Adaptive designs in implementation science and practice: their promise and the need for greater understanding and improved communication. Annu Rev Public Health, 2024, 45(1): 69-88.
15.	Mütze T, Glimm E, Schmidli H, et al. Group sequential designs with robust semiparametric recurrent event models. Stat Methods Med Res, 2019, 28(8): 2385-2403.
16.	Crowley J, Breslow N. Statistical analysis of survival data. Annu Rev Public Health, 1984, 5: 385-411.
17.	趙佳, 蔣志偉, 王陵, 等. 生存資料的成組序貫試驗樣本量估計及其PASS軟件實現. 中國衛生統計, 2015, 32(1): 165-167.
18.	Danzer MF, Terzer T, Berthold F, et al. Confirmatory adaptive group sequential designs for single-arm phase II studies with multiple time-to-event endpoints. Biom J, 2022, 64(2): 312-342.
19.	朱典, 王陵, 李嬋娟, 等. 基于指數分布的生存資料盲態下樣本量再估計方法. 中國衛生統計, 2018, 35(2): 162-167.
20.	NCSS. Group sequential log-rank test. Power analysis and sample size, version 11.
21.	蔣志偉. 臨床試驗中成組序貫設計的關鍵技術探討. 西安: 第四軍醫大學, 2013.
22.	Jiang Z, Wang L, Li C, et al. A practical simulation method to calculate sample size of group sequential trials for time-to-event data under exponential and Weibull distribution. PLoS One, 2012, 7(9): e44013.
23.	Filleron T, Gal J, Kramar A. Designing group sequential randomized clinical trials with time to event end points using a R function. Comput Methods Programs Biomed, 2012, 108(1): 113-128.
24.	Wassmer G, Bramath W. Group sequential and confirmatory adaptive designs in clinical trials. New York: Springer, 2016.
25.	Shi Y, Laud P, Neuner J. A dependent Dirichlet process model for survival data with competing risks. Lifetime Data Anal, 2021, 27(1): 156-176.
26.	White IR, Marley-Zagar E, Morris TP, et al. artcat: Sample-size calculation for an ordered categorical outcome. Stata J, 2023, 23(1): 3-23.
27.	PANTHER-Precision medicine Adaptive Network platform Trial in Hypoxemic acutE respiratory failuRe. 2025.
28.	周莎, 帥雨杏, 吉萌萌, 等. SMART兩階段設計與臨床試驗常用兩階段設計的比較. 中國循證醫學雜志, 2024, 24(12): 1482-1488.
29.	加依達爾·胡安, 姚明宏, 劉艷梅, 等. 貝葉斯統計在臨床試驗中的應用. 中國循證醫學雜志, 2025, 25(8): 952-959.
30.	Gao G, Gajewski BJ, Wick J, et al. Optimizing a Bayesian hierarchical adaptive platform trial design for stroke patients. Trials, 2022, 23(1): 754.

1. 黃曼麗, 李晨, 葛偉, 等. 臨床研究樣本代表性評估方法的對比研究. 中國衛生統計, 2024, 41(2): 167-174.
2. 潘岳松, 金奧銘, 王夢星. 臨床研究樣本量的估計方法和常見錯誤. 中國卒中雜志, 2022, 17(1): 31-35.
3. 國家藥品監督管理局藥品審評中心. 國家藥監局藥審中心關于發布《藥物臨床試驗適應性設計指導原則(試行)》的通告(2021年第6號). 2021.
4. 石巖巖, 張華, 趙一鳴. 臨床研究樣本量估計的參數選擇. 中華兒科雜志, 2024, 62(4): 350-352.
5. 王雨寧, 徐暢, 鄧可, 等. 適應性設計在臨床試驗中的應用. 中國循證醫學雜志, 2020, 20(4): 487-491.
6. Pallmann P, Bedding AW, Choodari-Oskooei B, et al. Adaptive designs in clinical trials: why use them, and how to run and report them. BMC Med, 2018, 16(1): 29.
7. U. S. Food and Drug Administration. Adaptive design clinical trials for drugs and biologics: guidance for industry. 2019.
8. 陳平雁. 臨床試驗中樣本量確定的統計學考慮. 中國衛生統計, 2015, 32(4): 727-731, 733.
9. 曾于珍, 陳世耀. 臨床研究結局指標選擇與樣本量估計. 協和醫學雜志, 2018, 9(1): 87-92.
10. Grayling MJ, Wheeler GM. A review of available software for adaptive clinical trial design. Clin Trials, 2020, 17(3): 323-331.
11. 林潔, 孫志明. SAS、PASS、Stata三種常用軟件樣本量計算方法及結果差異的比較. 中國醫藥導報, 2015, 12(18): 133-137.
12. Cui L, Hung HM, Wang SJ. Modification of sample size in group sequential clinical trials. Biometrics, 1999, 55(3): 853-857.
13. 傅利強, 陳思敏, 吳海燕, 等. 成組序貫設計中基于實際信息量的臨界值校正方法的比較研究. 中國衛生統計, 2022, 39(4): 542-544, 549.
14. Kilbourne A, Chinman M, Rogal S, et al. Adaptive designs in implementation science and practice: their promise and the need for greater understanding and improved communication. Annu Rev Public Health, 2024, 45(1): 69-88.
15. Mütze T, Glimm E, Schmidli H, et al. Group sequential designs with robust semiparametric recurrent event models. Stat Methods Med Res, 2019, 28(8): 2385-2403.
16. Crowley J, Breslow N. Statistical analysis of survival data. Annu Rev Public Health, 1984, 5: 385-411.
17. 趙佳, 蔣志偉, 王陵, 等. 生存資料的成組序貫試驗樣本量估計及其PASS軟件實現. 中國衛生統計, 2015, 32(1): 165-167.
18. Danzer MF, Terzer T, Berthold F, et al. Confirmatory adaptive group sequential designs for single-arm phase II studies with multiple time-to-event endpoints. Biom J, 2022, 64(2): 312-342.
19. 朱典, 王陵, 李嬋娟, 等. 基于指數分布的生存資料盲態下樣本量再估計方法. 中國衛生統計, 2018, 35(2): 162-167.
20. NCSS. Group sequential log-rank test. Power analysis and sample size, version 11.
21. 蔣志偉. 臨床試驗中成組序貫設計的關鍵技術探討. 西安: 第四軍醫大學, 2013.
22. Jiang Z, Wang L, Li C, et al. A practical simulation method to calculate sample size of group sequential trials for time-to-event data under exponential and Weibull distribution. PLoS One, 2012, 7(9): e44013.
23. Filleron T, Gal J, Kramar A. Designing group sequential randomized clinical trials with time to event end points using a R function. Comput Methods Programs Biomed, 2012, 108(1): 113-128.
24. Wassmer G, Bramath W. Group sequential and confirmatory adaptive designs in clinical trials. New York: Springer, 2016.
25. Shi Y, Laud P, Neuner J. A dependent Dirichlet process model for survival data with competing risks. Lifetime Data Anal, 2021, 27(1): 156-176.
26. White IR, Marley-Zagar E, Morris TP, et al. artcat: Sample-size calculation for an ordered categorical outcome. Stata J, 2023, 23(1): 3-23.
27. PANTHER-Precision medicine Adaptive Network platform Trial in Hypoxemic acutE respiratory failuRe. 2025.
28. 周莎, 帥雨杏, 吉萌萌, 等. SMART兩階段設計與臨床試驗常用兩階段設計的比較. 中國循證醫學雜志, 2024, 24(12): 1482-1488.
29. 加依達爾·胡安, 姚明宏, 劉艷梅, 等. 貝葉斯統計在臨床試驗中的應用. 中國循證醫學雜志, 2025, 25(8): 952-959.
30. Gao G, Gajewski BJ, Wick J, et al. Optimizing a Bayesian hierarchical adaptive platform trial design for stroke patients. Trials, 2022, 23(1): 754.

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