The application of Bayesian statistics in clinical trials_Chinese Journal of Evidence-Based Medicine

Authors：

JIAYIDAER Huan ^1,2,3,4 , YAO Minghong ^2,3,4 , LIU Yanmei ^2,3,4 , LIU Jiali ^2,3,4 ,  LI Ling ^2,3,4 ,  SUN Xin ^2,3,4

1. Department of Epidemiology and Biostatistics, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu 610041, P. R. China;
2. Chinese Evidence-based Medicine Center, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;
3. NMPA Key Laboratory for Real World Data Research and Evaluation in Hainan, Chengdu 610041, P. R. China;
4. Sichuan Center of Technology Innovation for Real World Data, Chengdu 610041, P. R. China;

Corresponding author：

LI Ling, Email: liling@wchscu.cn; SUN Xin, Email: sunxin@wchscu.cn

Keywords：

Bayesian methods; Bayesian statistics; Clinical development; Clinical trials

DOI：

10.7507/1672-2531.202503199

Video：

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Statistical analysis of clinical trials has traditionally relied on frequentist methods, but Bayesian statistics has attracted considerable attention from regulators and researchers in recent years due to its unique advantages, and its use in clinical trials is increasing. Despite the obvious advantages of Bayesian statistics, the complexity of its design, implementation and analysis poses a number of challenges to its practical application, which may lead to an increased risk of unregulated use. This study aims to comprehensively sort out the application scenarios, common methods, special considerations and key elements of reporting of Bayesian statistical methods in clinical trials, with the aim of providing researchers with references for conducting Bayesian clinical trials, and promoting the scientific and rational application of Bayesian statistical methods in clinical trials.

1.	Food and Drug Administration. The drug development process. 2020.
2.	An MW, Duong Q, Le-Rademacher J, et al. Principles of good clinical trial design. J Thorac Oncol, 2020, 15(8): 1277-1280.
3.	Yin G. Frequentist versus Bayesian statistics. Clinical Trial Design. New York: John Wiley & Sons, Ltd, 2011: 29-76.
4.	Gunn-Sandell LB, Bedrick EJ, Hutchins JL, et al. A practical guide to adopting Bayesian analyses in clinical research. J Clin Transl Sci, 2023, 8(1): e3.
5.	FDA. Using Bayesian statistical approaches to advance our ability to evaluate drug products. 2023.
6.	Muehlemann N, Zhou T, Mukherjee R, et al. A tutorial on modern Bayesian methods in clinical trials. Ther Innov Regul Sci, 2023, 57(3): 402-416.
7.	Goligher EC, Heath A, Harhay MO. Bayesian statistics for clinical research. Lancet, 2024, 404(10457): 1067-1076.
8.	Ruberg SJ, Beckers F, Hemmings R, et al. Application of Bayesian approaches in drug development: starting a virtuous cycle. Nat Rev Drug Discov, 2023, 22(3): 235-250.
9.	Gale JD, Gilbert S, Blumenthal S, et al. Effect of PF-04634817, an oral CCR2/5 chemokine receptor antagonist, on albuminuria in adults with overt diabetic nephropathy. Kidney Int Rep, 2018, 3(6): 1316-1327.
10.	Luce BR, Connor JT, Broglio KR, et al. Using Bayesian adaptive trial designs for comparative effectiveness research: a virtual trial execution. Ann Intern Med, 2016, 165(6): 431-438.
11.	Hobbs BP, Carlin BP, Sargent DJ. Adaptive adjustment of the randomization ratio using historical control data. Clin Trials, 2013, 10(3): 430-440.
12.	Skrivanek Z, Gaydos BL, Chien JY, et al. Dose-finding results in an adaptive, seamless, randomized trial of once-weekly dulaglutide combined with metformin in type 2 diabetes patients (AWARD-5). Diabetes Obes Metab, 2014, 16(8): 748-756.
13.	Ferreira D, Barthoulot M, Pottecher J, et al. Theory and practical use of Bayesian methods in interpreting clinical trial data: a narrative review. Br J Anaesth, 2020, 125(2): 201-207.
14.	Wijeysundera DN, Austin PC, Hux JE, et al. Bayesian statistical inference enhances the interpretation of contemporary randomized controlled trials. J Clin Epidemiol, 2009, 62(1): 13-21.
15.	O'Quigley J, Pepe M, Fisher L. Continual reassessment method: a practical design for phase 1 clinical trials in cancer. Biometrics, 1990, 46(1): 33-48.
16.	Babb J, Rogatko A, Zacks S. Cancer phase I clinical trials: efficient dose escalation with overdose control. Stat Med, 1998, 17(10): 1103-1120.
17.	Neuenschwander B, Branson M, Gsponer T. Critical aspects of the Bayesian approach to phase I cancer trials. Stat Med, 2008, 27(13): 2420-2439.
18.	Asakawa T, Hirakawa A, Hamada C. Bayesian model averaging continual reassessment method for bivariate binary efficacy and toxicity outcomes in phase I oncology trials. J Biopharm Stat, 2014, 24(2): 310-325.
19.	Ji Y, Liu P, Li Y, et al. A modified toxicity probability interval method for dose-finding trials. Clin Trials, 2010, 7(6): 653-663.
20.	Yan F, Mandrekar SJ, Yuan Y. Keyboard: A novel Bayesian toxicity probability interval design for phase i clinical trials. Clin Cancer Res, 2017, 23(15): 3994-4003.
21.	Yuan Y, Hess KR, Hilsenbeck SG, et al. Bayesian optimal interval design: a simple and well-performing design for phase I oncology trials. Clin Cancer Res, 2016, 22(17): 4291-4301.
22.	沈庆. 药物最大耐受剂量(MTD)探索方法的比较分析. 北京: 北京中医药大学, 2022.
23.	Zhou H, Yuan Y, Nie L. Accuracy, safety, and reliability of novel phase I trial designs. Clin Cancer Res, 2018, 24(18): 4357-4364.
24.	Lee JJ, Liu DD. A predictive probability design for phase II cancer clinical trials. Clin Trials, 2008, 5(2): 93-106.
25.	Zhou H, Lee JJ, Yuan Y. BOP2: Bayesian optimal design for phase II clinical trials with simple and complex endpoints. Stat Med, 2017, 36(21): 3302-3314.
26.	Lin R, Coleman RL, Yuan Y. TOP: Time-to-event bayesian optimal phase II trial design for cancer immunotherapy. J Natl Cancer Inst, 2020, 112(1): 38-45.
27.	Wu J, Pan H, Hsu CW. Bayesian single-arm phase II trial designs with time-to-event endpoints. Pharm Stat, 2021, 20(6): 1235-1248.
28.	Zhou H, Chen C, Sun L, et al. Bayesian optimal phase II clinical trial design with time-to-event endpoint. Pharm Stat, 2020, 19(6): 776-786.
29.	Khanna S, Assi M, Lee C, et al. Efficacy and safety of RBX2660 in PUNCH CD3, a phase III, randomized, double-blind, placebo-controlled trial with a Bayesian primary analysis for the prevention of recurrent clostridioides difficile Infection. Drugs, 2022, 82(15): 1527-1538.
30.	Ferreira D, Vivot A, Diemunsch P, et al. Bayesian analysis from phase III trials was underused and poorly reported: a systematic review. J Clin Epidemiol, 2020, 123: 107-113.
31.	Neumann K, Grittner U, Piper SK, et al. Increasing efficiency of preclinical research by group sequential designs. PLoS Biol, 2017, 15(3): e2001307.
32.	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.
33.	Rosenberger WF, Sverdlov O, Hu F. Adaptive randomization for clinical trials. J Biopharm Stat, 2012, 22(4): 719-736.
34.	Ahn C, Kang SH, Xie Y. Optimal biological dose for molecularly-targeted therapies. Wiley encyclopedia of clinical trials. New York: John Wiley & Sons, Ltd, 2007: 1-8.
35.	Shah M, Rahman A, Theoret MR, et al. The drug-dosing conundrum in oncology - when less is more. N Engl J Med, 2021, 385(16): 1445-1447.
36.	Reynolds AR. Potential relevance of bell-shaped and u-shaped dose-responses for the therapeutic targeting of angiogenesis in cancer. Dose Response, 2010, 8(3): 253-284.
37.	Ratain MJ, Tannock IF, Lichter AS. Dose optimization of sotorasib: is the US food and drug administration sending a message. J Clin Oncol, 2021, 39(31): 3423-3426.
38.	Yuan Y, Nguyen HQ, Thall PF. Bayesian designs for phase I-II clinical trials. New York: Chapman and Hall/CRC, 2016.
39.	Pan H, Yuan Y. Optimal biological dose and phase I/II trials. Bayesian adaptive design for immunotherapy and targeted therapy. Singapore: Springer Nature, 2023: 47-52.
40.	Thall PF, Cook JD. Dose-finding based on efficacy-toxicity trade-offs. Biometrics, 2004, 60(3): 684-693.
41.	Shi H, Cao J, Yuan Y, et al. uTPI: A utility-based toxicity probability interval design for phase I/II dose-finding trials. Stat Med, 2021, 40(11): 2626-2649.
42.	Yin J, Yuan Y. Checkerboard: a Bayesian efficacy and toxicity interval design for phase I/II dose-finding trials. J Biopharm Stat, 2020, 30(6): 1006-1025.
43.	Lin R, Zhou Y, Yan F, et al. BOIN12: Bayesian optimal interval phase I/II trial design for utility-based dose finding in immunotherapy and targeted therapies. JCO Precis Oncol, 2020, 4: PO. 20.00257.
44.	Yuan Y, Zhou H, Liu S. Statistical and practical considerations in planning and conduct of dose-optimization trials. Clin Trials, 2024, 21(3): 273-286.
45.	Zhou Y, Lee JJ, Yuan Y. A utility-based Bayesian optimal interval (U-BOIN) phase I/II design to identify the optimal biological dose for targeted and immune therapies. Stat Med, 2019, 38(28): 5299-5316.
46.	Yang J, Li G, Yang D, et al. Seamless phase 2/3 design for trials with multiple co-primary endpoints using Bayesian predictive power. BMC Med Res Methodol, 2024, 24(1): 12.
47.	Wang X, Chen M, Chu S, et al. A rank-based approach to improve the efficiency of inferential seamless phase 2/3 clinical trials with dose optimization. Contemp Clin Trials, 2023, 132: 107300.
48.	Cecchini M, Rubin EH, Blumenthal GM, et al. Challenges with novel clinical trial designs: master protocols. Clin Cancer Res, 2019, 25(7): 2049-2057.
49.	Woodcock J, LaVange LM. Master protocols to study multiple therapies, multiple diseases, or both. N Engl J Med, 2017, 377(1): 62-70.
50.	Chen C, Hsiao CF. Bayesian hierarchical models for adaptive basket trial designs. Pharm Stat, 2023, 22(3): 531-546.
51.	Ouma LO, Wason JMS, Zheng H, et al. Design and analysis of umbrella trials: where do we stand. Front Med (Lausanne), 2022, 9: 1037439.
52.	Park JJH, Hsu G, Siden EG, et al. An overview of precision oncology basket and umbrella trials for clinicians. CA Cancer J Clin, 2020, 70(2): 125-137.
53.	Griessbach A, Schönenberger CM, Taji Heravi A, et al. Characteristics, progression, and output of randomized platform trials: a systematic review. JAMA Netw Open, 2024, 7(3): e243109.
54.	Rietbergen C, Debray TPA, Klugkist I, et al. Reporting of Bayesian analysis in epidemiologic research should become more transparent. J Clin Epidemiol, 2017, 86: 51-58.
55.	Kruschke JK. Bayesian analysis reporting guidelines. Nat Hum Behav, 2021, 5(10): 1282-1291.
56.	Iglesias JF, Muller O, Heg D, et al. Biodegradable polymer sirolimus-eluting stents versus durable polymer everolimus-eluting stents in patients with ST-segment elevation myocardial infarction (BIOSTEMI): a single-blind, prospective, randomised superiority trial. Lancet, 2019, 394(10205): 1243-1253.
57.	Sung L, Hayden J, Greenberg ML, et al. Seven items were identified for inclusion when reporting a Bayesian analysis of a clinical study. J Clin Epidemiol, 2005, 58(3): 261-268.
58.	Lee JJ, Yin G. Principles and reporting of Bayesian trials. J Thorac Oncol, 2021, 16(1): 30-36.
59.	Ferreira D, Barthoulot M, Pottecher J, et al. A consensus checklist to help clinicians interpret clinical trial results analysed by Bayesian methods. Br J Anaesth, 2020, 125(2): 208-215.
60.	FDA. Guidance for the use of Bayesian statistics in medical device clinical trials. 2020.
61.	Spiegelhalter DJ, Myles JP, Jones DR, et al. Bayesian methods in health technology assessment: a review. Health Technol Assess, 2000, 4(38): 1-130.
62.	Gatsonis C, Goodman S. Bayesian standards in science-standards for reporting of Bayesian analyses in the scientific literature. The BaSiS Group, 2001.
63.	Pibouleau L, Chevret S. Bayesian statistical method was underused despite its advantages in the assessment of implantable medical devices. J Clin Epidemiol, 2011, 64(3): 270-279.
64.	Wang Y, Travis J, Gajewski B. Bayesian adaptive design for pediatric clinical trials incorporating a community of prior beliefs. BMC Med Res Methodol, 2022, 22(1): 118.

1. Food and Drug Administration. The drug development process. 2020.
2. An MW, Duong Q, Le-Rademacher J, et al. Principles of good clinical trial design. J Thorac Oncol, 2020, 15(8): 1277-1280.
3. Yin G. Frequentist versus Bayesian statistics. Clinical Trial Design. New York: John Wiley & Sons, Ltd, 2011: 29-76.
4. Gunn-Sandell LB, Bedrick EJ, Hutchins JL, et al. A practical guide to adopting Bayesian analyses in clinical research. J Clin Transl Sci, 2023, 8(1): e3.
5. FDA. Using Bayesian statistical approaches to advance our ability to evaluate drug products. 2023.
6. Muehlemann N, Zhou T, Mukherjee R, et al. A tutorial on modern Bayesian methods in clinical trials. Ther Innov Regul Sci, 2023, 57(3): 402-416.
7. Goligher EC, Heath A, Harhay MO. Bayesian statistics for clinical research. Lancet, 2024, 404(10457): 1067-1076.
8. Ruberg SJ, Beckers F, Hemmings R, et al. Application of Bayesian approaches in drug development: starting a virtuous cycle. Nat Rev Drug Discov, 2023, 22(3): 235-250.
9. Gale JD, Gilbert S, Blumenthal S, et al. Effect of PF-04634817, an oral CCR2/5 chemokine receptor antagonist, on albuminuria in adults with overt diabetic nephropathy. Kidney Int Rep, 2018, 3(6): 1316-1327.
10. Luce BR, Connor JT, Broglio KR, et al. Using Bayesian adaptive trial designs for comparative effectiveness research: a virtual trial execution. Ann Intern Med, 2016, 165(6): 431-438.
11. Hobbs BP, Carlin BP, Sargent DJ. Adaptive adjustment of the randomization ratio using historical control data. Clin Trials, 2013, 10(3): 430-440.
12. Skrivanek Z, Gaydos BL, Chien JY, et al. Dose-finding results in an adaptive, seamless, randomized trial of once-weekly dulaglutide combined with metformin in type 2 diabetes patients (AWARD-5). Diabetes Obes Metab, 2014, 16(8): 748-756.
13. Ferreira D, Barthoulot M, Pottecher J, et al. Theory and practical use of Bayesian methods in interpreting clinical trial data: a narrative review. Br J Anaesth, 2020, 125(2): 201-207.
14. Wijeysundera DN, Austin PC, Hux JE, et al. Bayesian statistical inference enhances the interpretation of contemporary randomized controlled trials. J Clin Epidemiol, 2009, 62(1): 13-21.
15. O'Quigley J, Pepe M, Fisher L. Continual reassessment method: a practical design for phase 1 clinical trials in cancer. Biometrics, 1990, 46(1): 33-48.
16. Babb J, Rogatko A, Zacks S. Cancer phase I clinical trials: efficient dose escalation with overdose control. Stat Med, 1998, 17(10): 1103-1120.
17. Neuenschwander B, Branson M, Gsponer T. Critical aspects of the Bayesian approach to phase I cancer trials. Stat Med, 2008, 27(13): 2420-2439.
18. Asakawa T, Hirakawa A, Hamada C. Bayesian model averaging continual reassessment method for bivariate binary efficacy and toxicity outcomes in phase I oncology trials. J Biopharm Stat, 2014, 24(2): 310-325.
19. Ji Y, Liu P, Li Y, et al. A modified toxicity probability interval method for dose-finding trials. Clin Trials, 2010, 7(6): 653-663.
20. Yan F, Mandrekar SJ, Yuan Y. Keyboard: A novel Bayesian toxicity probability interval design for phase i clinical trials. Clin Cancer Res, 2017, 23(15): 3994-4003.
21. Yuan Y, Hess KR, Hilsenbeck SG, et al. Bayesian optimal interval design: a simple and well-performing design for phase I oncology trials. Clin Cancer Res, 2016, 22(17): 4291-4301.
22. 沈庆. 药物最大耐受剂量(MTD)探索方法的比较分析. 北京: 北京中医药大学, 2022.
23. Zhou H, Yuan Y, Nie L. Accuracy, safety, and reliability of novel phase I trial designs. Clin Cancer Res, 2018, 24(18): 4357-4364.
24. Lee JJ, Liu DD. A predictive probability design for phase II cancer clinical trials. Clin Trials, 2008, 5(2): 93-106.
25. Zhou H, Lee JJ, Yuan Y. BOP2: Bayesian optimal design for phase II clinical trials with simple and complex endpoints. Stat Med, 2017, 36(21): 3302-3314.
26. Lin R, Coleman RL, Yuan Y. TOP: Time-to-event bayesian optimal phase II trial design for cancer immunotherapy. J Natl Cancer Inst, 2020, 112(1): 38-45.
27. Wu J, Pan H, Hsu CW. Bayesian single-arm phase II trial designs with time-to-event endpoints. Pharm Stat, 2021, 20(6): 1235-1248.
28. Zhou H, Chen C, Sun L, et al. Bayesian optimal phase II clinical trial design with time-to-event endpoint. Pharm Stat, 2020, 19(6): 776-786.
29. Khanna S, Assi M, Lee C, et al. Efficacy and safety of RBX2660 in PUNCH CD3, a phase III, randomized, double-blind, placebo-controlled trial with a Bayesian primary analysis for the prevention of recurrent clostridioides difficile Infection. Drugs, 2022, 82(15): 1527-1538.
30. Ferreira D, Vivot A, Diemunsch P, et al. Bayesian analysis from phase III trials was underused and poorly reported: a systematic review. J Clin Epidemiol, 2020, 123: 107-113.
31. Neumann K, Grittner U, Piper SK, et al. Increasing efficiency of preclinical research by group sequential designs. PLoS Biol, 2017, 15(3): e2001307.
32. 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.
33. Rosenberger WF, Sverdlov O, Hu F. Adaptive randomization for clinical trials. J Biopharm Stat, 2012, 22(4): 719-736.
34. Ahn C, Kang SH, Xie Y. Optimal biological dose for molecularly-targeted therapies. Wiley encyclopedia of clinical trials. New York: John Wiley & Sons, Ltd, 2007: 1-8.
35. Shah M, Rahman A, Theoret MR, et al. The drug-dosing conundrum in oncology - when less is more. N Engl J Med, 2021, 385(16): 1445-1447.
36. Reynolds AR. Potential relevance of bell-shaped and u-shaped dose-responses for the therapeutic targeting of angiogenesis in cancer. Dose Response, 2010, 8(3): 253-284.
37. Ratain MJ, Tannock IF, Lichter AS. Dose optimization of sotorasib: is the US food and drug administration sending a message. J Clin Oncol, 2021, 39(31): 3423-3426.
38. Yuan Y, Nguyen HQ, Thall PF. Bayesian designs for phase I-II clinical trials. New York: Chapman and Hall/CRC, 2016.
39. Pan H, Yuan Y. Optimal biological dose and phase I/II trials. Bayesian adaptive design for immunotherapy and targeted therapy. Singapore: Springer Nature, 2023: 47-52.
40. Thall PF, Cook JD. Dose-finding based on efficacy-toxicity trade-offs. Biometrics, 2004, 60(3): 684-693.
41. Shi H, Cao J, Yuan Y, et al. uTPI: A utility-based toxicity probability interval design for phase I/II dose-finding trials. Stat Med, 2021, 40(11): 2626-2649.
42. Yin J, Yuan Y. Checkerboard: a Bayesian efficacy and toxicity interval design for phase I/II dose-finding trials. J Biopharm Stat, 2020, 30(6): 1006-1025.
43. Lin R, Zhou Y, Yan F, et al. BOIN12: Bayesian optimal interval phase I/II trial design for utility-based dose finding in immunotherapy and targeted therapies. JCO Precis Oncol, 2020, 4: PO. 20.00257.
44. Yuan Y, Zhou H, Liu S. Statistical and practical considerations in planning and conduct of dose-optimization trials. Clin Trials, 2024, 21(3): 273-286.
45. Zhou Y, Lee JJ, Yuan Y. A utility-based Bayesian optimal interval (U-BOIN) phase I/II design to identify the optimal biological dose for targeted and immune therapies. Stat Med, 2019, 38(28): 5299-5316.
46. Yang J, Li G, Yang D, et al. Seamless phase 2/3 design for trials with multiple co-primary endpoints using Bayesian predictive power. BMC Med Res Methodol, 2024, 24(1): 12.
47. Wang X, Chen M, Chu S, et al. A rank-based approach to improve the efficiency of inferential seamless phase 2/3 clinical trials with dose optimization. Contemp Clin Trials, 2023, 132: 107300.
48. Cecchini M, Rubin EH, Blumenthal GM, et al. Challenges with novel clinical trial designs: master protocols. Clin Cancer Res, 2019, 25(7): 2049-2057.
49. Woodcock J, LaVange LM. Master protocols to study multiple therapies, multiple diseases, or both. N Engl J Med, 2017, 377(1): 62-70.
50. Chen C, Hsiao CF. Bayesian hierarchical models for adaptive basket trial designs. Pharm Stat, 2023, 22(3): 531-546.
51. Ouma LO, Wason JMS, Zheng H, et al. Design and analysis of umbrella trials: where do we stand. Front Med (Lausanne), 2022, 9: 1037439.
52. Park JJH, Hsu G, Siden EG, et al. An overview of precision oncology basket and umbrella trials for clinicians. CA Cancer J Clin, 2020, 70(2): 125-137.
53. Griessbach A, Schönenberger CM, Taji Heravi A, et al. Characteristics, progression, and output of randomized platform trials: a systematic review. JAMA Netw Open, 2024, 7(3): e243109.
54. Rietbergen C, Debray TPA, Klugkist I, et al. Reporting of Bayesian analysis in epidemiologic research should become more transparent. J Clin Epidemiol, 2017, 86: 51-58.
55. Kruschke JK. Bayesian analysis reporting guidelines. Nat Hum Behav, 2021, 5(10): 1282-1291.
56. Iglesias JF, Muller O, Heg D, et al. Biodegradable polymer sirolimus-eluting stents versus durable polymer everolimus-eluting stents in patients with ST-segment elevation myocardial infarction (BIOSTEMI): a single-blind, prospective, randomised superiority trial. Lancet, 2019, 394(10205): 1243-1253.
57. Sung L, Hayden J, Greenberg ML, et al. Seven items were identified for inclusion when reporting a Bayesian analysis of a clinical study. J Clin Epidemiol, 2005, 58(3): 261-268.
58. Lee JJ, Yin G. Principles and reporting of Bayesian trials. J Thorac Oncol, 2021, 16(1): 30-36.
59. Ferreira D, Barthoulot M, Pottecher J, et al. A consensus checklist to help clinicians interpret clinical trial results analysed by Bayesian methods. Br J Anaesth, 2020, 125(2): 208-215.
60. FDA. Guidance for the use of Bayesian statistics in medical device clinical trials. 2020.
61. Spiegelhalter DJ, Myles JP, Jones DR, et al. Bayesian methods in health technology assessment: a review. Health Technol Assess, 2000, 4(38): 1-130.
62. Gatsonis C, Goodman S. Bayesian standards in science-standards for reporting of Bayesian analyses in the scientific literature. The BaSiS Group, 2001.
63. Pibouleau L, Chevret S. Bayesian statistical method was underused despite its advantages in the assessment of implantable medical devices. J Clin Epidemiol, 2011, 64(3): 270-279.
64. Wang Y, Travis J, Gajewski B. Bayesian adaptive design for pediatric clinical trials incorporating a community of prior beliefs. BMC Med Res Methodol, 2022, 22(1): 118.

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