Adaptive Clinical Trials: Progress and Challenges

Drugs in R and D

Volumn 9, Issue 4, 2008, Pages 229-242

  Coffey, Christopher S ^a   Kairalla, John A ^b  

^a UNIVERSITY OF ALABAMA AT BIRMINGHAM   (United States)

^b UNIVERSITY OF FLORIDA COLLEGE OF MEDICINE   (United States)

Author keywords

Clinical trial design; Drug evaluation

Indexed keywords

BAYES THEOREM; CLINICAL TRIAL; DOSE RESPONSE; DRUG DEVELOPMENT; EFFECT SIZE; FUNDING; MAXIMUM TOLERATED DOSE; PRIORITY JOURNAL; RANDOMIZATION; REVIEW; SAMPLE SIZE; STATISTICS;

CLINICAL TRIALS AS TOPIC; CLINICAL TRIALS, PHASE III AS TOPIC; DATA INTERPRETATION, STATISTICAL; DRUG DESIGN; HUMANS; PHARMACOLOGY; PILOT PROJECTS; SAMPLE SIZE;

EID: 46349091495     PISSN: 11745886     EISSN: None     Source Type: Journal    
DOI: 10.2165/00126839-200809040-00003     Document Type: Review

References (99)

1. Wittes J, Brittain E. The role of internal pilot studies in increasing the efficiency of clinical trials. Stat Med 1990; 9: 65-72
2. Gallo P, Chuang-Stein C, Dragalin V, et al. Adaptive designs in clinical drug development: an executive summary of the PhRMA working group. J Biopharm Stat 2006; 16: 275-83
3. Whitehead J, Zhou Y, Patterson S, et al. Easy-to-implement Bayesian methods for dose-escalation studies in healthy volunteers. Biostatistics 2001; 2: 47-61
4. Krams M, Lees KR, Berry DA. The past is the future: innovative designs in acute stroke therapy trials. Stroke 2005; 36: 1341-7
5. Gaydos B, Krams M, Perevozskaya I, et al. Adaptive dose-response studies. Drug Inf J 2006; 40: 451-61
6. Maca J, Bhattacharya S, Dragalin V, et al. Adaptive seamless phase II/III designs: background, operational aspects, and examples. Drug Inf J 2006; 40: 463-73
7. Chow S, Chang M. Adaptive design methods in clinical trials. Boca Raton (FL): Chapman & Hall/CRC, 2007
8. Dragalin V. Adaptive designs: terminology and classification. Drug Inf J 2006; 40: 425-35
9. Chang M. Adaptive design theory and implementation using SAS and R. Boca Raton (FL): Chapman & Hall/CRC, 2008
10. Burman CF, Sonesson C. Are flexible designs sound? Biometrics 2006; 62: 664-83
11. O'Quigley J, Pepe M, Fisher L. Continual reassessment method: a practical design for phase I clinical trials in cancer. Biometrics 1990; 46: 33-48
12. Garrett-Moyer E. The continual reassessment method for dose-finding studies: a tutorial. Clin Trials 2006; 3: 57-71
13. Dougherty TB, Porche VH, Thall PF. Maximum tolerated dose of nalmefene in patients receiving epidural fentanyl and dilute bupivacaine for postoperative analgesia. Anesthesiology 2000; 92: 1010-6
14. Desfrere L, Zohar S, Morville P, et al. Dose-finding study of ibuprofen in patent ductus arteriosus using the continual reassessment method. J Clin Pharm Ther 2005; 30: 121-32
15. Beloeil H, Eurin M, Thevenin A, et al. Effective dose of nefopam in 80% of patients (ED80): a study using the continual reassessment method. Br J Clin Pharmacol 2007; 64: 686-93
16. Thevenin A, Beloeil H, Blanie A, et al. The limited efficacy of tramadol in post-operative patients: a study of ED80 using the continual reassessment method. Anesth Analg 2008; 106: 622-7
17. Bornkamp B, Bretz F, Dmitrienko A, et al. Innovative approaches for designing and analyzing adaptive dose-ranging trials. J Biopharm Stat 2008; 17: 965-95
18. Berry DA, Mueller P, Grieve AP, et al. Bayesian designs for dose-ranging drug trials. In: Gatsonis C, Kass RE, Carlin B, et al., editors. Case studies in Bayesian statistics, Vol. 5. New York (NY): Springer-Verlag, 2002: 99-181
19. Krams M, Lees KR, Hacke W, et al. ASTIN: an adaptive dose-response study of UK-279,276 in acute ischemic stroke. Stroke 2003; 34: 2543-9
20. Levy G, Kaufmann P, Buchsbaum R, et al. A two-stage design for a phase II clinical trial of coenzyme Q10 in ALS. Neurology 2006; 66: 660-3
21. Bretz F, Schmidli H, Koenig F, et al. Confirmatory seamless phase II/III clinical trials with hypothesis selection at interim: general concepts. Biom J 2006; 48 (4): 623-34
22. Bauer P, Kieser M. Combining different phases in development of medical treatments within a single trial. Stat Med 1999; 18: 1833-48
23. Inoue LYT, Thall PF, Berry DA. Seamlessly expanding a randomized phase II trial to phase III. Biometrics 2002; 58: 823-31
24. Bauer P, Kohne K. Evaluation of experiments with adaptive interim analyses. Biometrics 1994; 50: 1029-41
25. Muller HH, Schafer H. Adaptive group sequential designs for clinical trials: combining the advantages of adaptive and classical group sequential approaches. Biometrics 2001; 57: 886-91
26. Ellenberg SS, Fleming TR, DeMets DL. Data monitoring committees in clinical trials: a practical perspective. Hoboken (NJ): John Wiley & Sons, 2003
27. Proschan MA, Lan KKG, Wittes J. Statistical monitoring of clinical trials: a unified approach. New York (NY): Springer, 2006
28. Jennison C, Turnbull BW. Group sequential methods with applications to clinical trials. Boca Raton (FL): Chapman & Hall/CRC, 2000
29. Rosenberger WF, Lachin J. Randomization in clinical trials. New York (NY): John Wiley & Sons, 2002
30. Zelen M. The randomization and stratification of patients to clinical trials. J Chronic Dis 1974; 28: 365-75
31. Pocock SJ, Simon R. Sequential treatment assignment with balancing for prognostic factors in the controlled clinical trials. Biometrics 1975; 31: 103-15
32. Wei LJ, Durham S. The randomized play-the-winner rule in medical trials. J Am Stat Assoc 1978; 73: 840-843
33. Hardwick JP, Stout QF. Bandit strategies for ethical sequential allocation. Computing Science Stat 1991; 23: 421-4
34. Coad DS, Rosenberger WF. A comparison of the randomized play-the-winner and the triangular test for clinical trials with binary responses. Stat Med 1999; 18: 761-9
35. Bartlett RH, Roloff DW, Cornell RG, et al. Extracorporeal circulation in neonatal respiratory failure: a prospective randomized study. Pediatrics 1985; 76: 479-87
36. Freedman KB, Bernstein J. Sample size and statistical power in clinical orthopaedic research. J Bone Joint Surgery 1999; 81: 1454-60
37. Dickinson K, Bunn F, Wentz R, et al. Size and quality of randomized controlled trials in head injury: review of published studies. BMJ 2000; 320: 1308-11
38. Samsa GP, Matchar DB. Have randomized controlled trials of neuroprotective drugs been underpowered? An illustration of three statistical principles. Stroke 2001; 32: 669-74
39. Furlan AJ. Acute stroke trials: strengthening the underpowered. Stroke 2002; 33: 1450-1
40. Maxwell SE. The persistence of underpowered studies in psychological research: causes, consequences, and remedies. Psychol Methods 2004; 9: 147-63
41. Wakelee H, Dubey S, Gandara D. Optimal adjuvant therapy for non-small cell lung cancer: how to handle stage I disease. Oncologist 2007; 12: 331-7
42. Bedard PL, Krzyanowska MK, Pintilie M, et al. Statistical power of negative randomized controlled trials presented at American Society for Clinical Oncology annual meetings. J Clin Oncol 2007; 25: 3482-6
43. Edwards SJL, Lilford RJ, Braunholtz D, et al. Why 'under-powered' trials are not necessarily unethical. Lancet 1997; 350: 804-7
44. Bachetti P, Wolf LE, Segal MR, et al. Ethics and sample size. Am J Epidemiol 2005; 161: 105-10
45. Halpern S, Karlawish JHT, Berlin JA. The continuing unethical conduct of underpowered trials. JAMA 2002; 288: 358-62
46. Bäuer P, Köhne K. Evaluation of experiments with adaptive interim analyses. Biometrics 1994; 50: 1029-41
47. Proschan MA, Hunsberger SA. Designed extension of studies based on conditional power. Biometrics 1995; 51: 1315-24
48. Fisher DL. Self-designing clinical trials. Stat Med 1998; 17: 1551-62
49. Lehmacher W, Wassmer G. Adaptive sample size calculations in group sequential trials. Biometrics 1999; 55: 1286-90
50. Cui L, Hung HMJ, Wang S. Modification of sample size in group sequential clinical trials. Biometrics 1999; 55: 853-7
51. Müller H, Schäfer H. Adaptive group sequential designs for clinical trials: combining the advantages of adaptive and of classical group sequential approaches. Biometrics 2001; 57: 886-91
52. Denne JS. Sample size recalculation using conditional power. Stat Med 2001; 20: 2645-60
53. Mehta CR, Patel NR. Adaptive, group sequential, and decision theoretic approaches to sample size determination. Stat Med 2006; 25: 3250-69
54. Tsiatis AA, Mehta C. On the inefficiency of the adaptive design for monitoring clinical trials. Biometrika 2003; 90: 367-78
55. Jennison C, Turnbull BW. Efficient group sequential designs when there are several effect sizes under consideration. Stat Med 2006; 25: 917-32
56. Herson J, Wittes J. The use of interim analysis in sample size adjustment. Drug Inf J 1993; 27: 753-60
57. Gould AL. Interim analyses for monitoring clinical trials that do not materially affect the type I error rate. Stat Med 1992; 11: 55-66
58. Bolland K, Sooriyarachchi MR, Whitehead J. Sample size review in a head injury trial with ordered categorical responses. Stat Med 1998; 17: 2835-47
59. Whitehead J, Whitehead A, Todd S, et al. Mid-trial design reviews for sequential clinical trials. Stat Med 2001; 20: 165-76
60. Coffey CS, Muller KE. Exact test size and power of a Gaussian error linear model for an internal pilot study. Stat Med 1999; 18: 1199-214
61. Coffey CS, Muller KE. Controlling test size while gaining the benefits of an internal pilot design. Biometrics 2001; 57: 625-31
62. Coffey CS, Kairalla JA, Muller KE. Practical methods for bounding type I error rate with an internal pilot design. Comm Stat Theory Methods 2007; 36: 2143-57
63. Shih WJ, Gould AL. Re-evaluating design specifications of longitudinal clinical trials without unblinding when the key response is rate of change. Stat Med 1995; 14: 2239-48
64. Lake S, Kammann E, Klar N, et al. Sample size re-estimation in cluster randomization trials. Stat Med 2002; 21: 1337-50
65. Zucker DM, Denne J. Sample size re-determination for repeated measures studies. Biometrics 2002; 58: 548-59
66. Coffey CS, Muller KE. Properties of internal pilots with the univariate approach to repeated measures. Stat Med 2003; 22: 2469-85
67. Gurka MJ, Coffey CS, Muller KE. Internal pilots for a class of linear mixed models with Gaussian and compound symmetric data. Stat Med 2007; 26: 4083-99
68. Proschan MA. Two-stage sample size re-estimation based on a nuisance parameter: a review. J Biopharm Stat 2005; 15: 559-74
69. Friede T, Kieser M. Sample size recalculation in internal pilot study designs: a review. Biom J 2006; 4: 537-55
70. NIH (National Institutes of Health). Roadmap for medical research [online]. Available from URL: http://nihroadmap.nih.gov [Accessed 2008 Jun 12]
71. Gould AL, Shih W. Sample size re-estimation without unblindning for normally distributed outcomes with unknown variance. Comm Stat Theory Methods 1992; 21: 2833-53
72. Denne JS, Jennison C. Estimating the sample size for a t-test using an internal pilot. Stat Med 1999; 18: 1575-85
73. Zucker DM, Wittes JT, Schabenberger O, et al. Internal pilot studies. II: comparison of various procedures. Stat Med 1999; 18: 3493-509
74. Kieser M, Friede T. Re-calculating the sample size in internal pilot study designs with control of the type I error rate. Stat Med 2000; 19: 901-11
75. Proschan MA, Wittes J. An improved double sampling procedure based on the variance. Biometrics 2000; 56: 1183-7
76. Kieser M, Friede T. Simple procedures for blinded sample size adjustment that do not affect the type I error rate. Stat Med 2003; 22: 3571-81
77. Miller F. Variance estimation in clinical studies with interim sample size re-estimation. Biometrics 2005; 61: 355-61
78. ICH Guideline E9. International conference on harmonisation of technical requirements for registration of pharmaceuticals for human use. ICH Topic E9: statistical principles for clinical trials. London: ICH Technical Coordination, EMEA, 1998
79. Bauer P, Einfalt J. Application of adaptive designs: a review. Biometric J 2006; 4: 493-506
80. Tharmanathan P, Calvert M, Hampton J, et al. The use of interim data and data monitoring committee recommendations in randomized controlled trial reports: frequency, implications, and potential sources of bias. BMC Med Res Methodol 2008; 8: 12
81. Hersey P, Coates AS, McCarthy WH, et al. Adjuvant immuno-therapy of patients with high-risk melanoma using vaccinia viral lysates of melanoma: results of a randomized trial. J Clin Oncol 2002; 20: 4181-90
82. To MS, Alfirevic Z, Heath VCF, et al. Cervical cerclage for prevention of preterm delivery in women with short cervix: randomised controlled trial. Lancet 2004; 363: 1849-53
83. Brennan DC, Daller JA, Lake KD, et al. Rabbit antithymocyte globulin versus basiliximab in renal transplantation. N Engl J Med 2006; 355: 1967-77
84. Pepine CJ, Handberg EM, Cooper-DeHoff RM, et al. A calcium antagonist vs. a non-calcium antagonist hypertension treatment strategy for patients with coronary artery disease - the International Verapamil-Trandolapril Study (INVEST): a randomized controlled trial. JAMA 2003; 290: 2805-16
85. Kirby S, McBride S, Puvanarajan L. An example of an unblended, third-party interim analysis for sample size re-stimation. Drug Inf J 2003; 37: 317-20
86. Mehta CR, Tsiatis AA. Flexible sample size considerations using information based monitoring. Drug Inf J 2001; 35: 1095-112
87. Tsiatis AA. Information based monitoring of clinical trials. Stat Med 2006; 25: 3236-44
88. Kairalla JA. An internal pilot study with interim analysis for Gaussian linear models [dissertation]. Chapel Hill (NC): University of North Carolina at Chapel Hill, 2007
89. Quinlan JA, Krams M. Implementing adaptive designs: logistical and operational considerations. Drug Inf J 2006; 40 (6): 437-44
90. Hung HMJ, O'Neill RT, Wang SJ, et al. A regulatory view on adaptive/flexible clinical trial design. Biom J 2006; 3: 1-9
91. Fisher MR, Roecker EB, Demets DL. The role of an independent statistical analysis center in the industry-modified National Institutes of Health model. Drug Inf J 2001; 35: 115-29
92. Bryant J. What is the appropriate role of the trial statistician in preparing and presenting interim findings to an independent data monitoring committee in the U.S. Cancer Cooperative Group setting? Stat Med 2004; 23: 1507-11
93. D'Agostino RB. The statistician and the data monitoring committee. Stat Med 2004; 23: 1501-2
94. Ellenberg SS, George SL. Should statisticians reporting to data monitoring committees be independent of the trial sponsor and leadership? Stat Med 2004; 23: 1503-5
95. Pocock SJ. A major trial needs three statisticians: why, how, and who? Stat Med 2004; 23: 1535-9
96. Snappin S, Cook T, Shapiro D, et al. The role of the unblinded sponsor statistician. Stat Med 2004; 23: 1531-3
97. Wittes J. Playing safe and preserving integrity: making the FDA model work. Stat Med 2004; 23: 1523-5
98. Coffey CS, Muller KE. GLUMIP 1.0: Free SAS/IML software for internal pilots. 2001 Proceedings of the Joint Statistical Meetings, Biopharmaceutical Section [CD-ROM]
99. EMEA. Report on the EMEA-EFPIA workshop on adaptive designs in confirmatory clinical trials. European Medicines Agency 2008, EMEA/106659/2008