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Methodology and Application of Adaptive and Sequential Approaches in Contemporary Clinical Trials

DOI: 10.1155/2012/527351

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Abstract:

The clinical trial, a prospective study to evaluate the effect of interventions in humans under prespecified conditions, is a standard and integral part of modern medicine. Many adaptive and sequential approaches have been proposed for use in clinical trials to allow adaptations or modifications to aspects of a trial after its initiation without undermining the validity and integrity of the trial. The application of adaptive and sequential methods in clinical trials has significantly improved the flexibility, efficiency, therapeutic effect, and validity of trials. To further advance the performance of clinical trials and convey the progress of research on adaptive and sequential methods in clinical trial design, we review significant research that has explored novel adaptive and sequential approaches and their applications in Phase I, II, and III clinical trials and discuss future directions in this field of research. 1. Clinical Trials Medicine is of paramount importance for human healthcare. Development of novel successful medicines is a lengthy, difficult, and expensive process which consists of laboratory experimentation, animal studies, clinical trials (Phase I, II, and III), and postmarket followup (Phase IV). Clinical trials are FDA-approved studies conducted in human beings to demonstrate the safety and efficacy of new drugs for health interventions under pre-specified conditions. A clinical trial is conducted in a sampled small population and the conclusions reached will be applied to a whole target population; therefore, statistics is an indispensable and critical component of clinical trial development and analysis, which has become increasingly important in contemporary clinical trials. As the gold standard for the evaluation of a new drug, every contemporary clinical trial must be well designed according to its specific purpose and conducted properly under governmental regulations. The major roles of a statistician in a clinical trial are to design an efficient trial with minimum cost and length and maximum therapeutic effect for patients in the trial, and to draw convincing conclusions by applying appropriate cutting edge statistical knowledge. In the past several decades, numerous groundbreaking novel statistical methodologies have been developed and applied to clinical trials and have significantly improved their performance. Consequently, clinical trials have evolved from simple observation studies to hypothesis-driven and well-designed prospective studies. At present, contemporary clinical trials have become the most important part of

References

[1]  S. C. Chow and M. Chang, “Adaptive design methods in clinical trials—a review,” Orphanet Journal of Rare Diseases, vol. 3, no. 1, article 11, 2008.
[2]  P. Gallo, C. Chuang-Stein, V. Dragalin, B. Gaydos, M. Krams, and J. Pinheiro, “Adaptive designs in clinical drug development—an executive summary of the PhRMA working group,” Journal of Biopharmaceutical Statistics, vol. 16, no. 3, pp. 275–283, 2006.
[3]  D. M. Potter, “Phase I studies of chemotherapeutic agents in cancer patients: a review of the designs,” Journal of Biopharmaceutical Statistics, vol. 16, no. 5, pp. 579–604, 2006.
[4]  Y. Lin and W. J. Shih, “Statistical properties of the traditional algorithm-based designs for phase I cancer clinical trials,” Biostatistics, vol. 2, no. 2, pp. 203–215, 2001.
[5]  D. H. Y. Leung and Y. G. Wang, “Isotonic designs for phase I trials,” Controlled Clinical Trials, vol. 22, no. 2, pp. 126–138, 2001.
[6]  R. Simon, B. Freidlin, L. Rubinstein, S. G. Arbuck, J. Collins, and M. C. Christian, “Accelerated titration designs for phase I clinical trials in oncology,” Journal of the National Cancer Institute, vol. 89, no. 15, pp. 1138–1147, 1997.
[7]  B. E. Storer, “Design and analysis of phase I clinical trials,” Biometrics, vol. 45, no. 3, pp. 925–937, 1989.
[8]  J. O'Quigley, M. Pepe, and L. Fisher, “Continual reassessment method: a practical design for phase 1 clinical trials in cancer,” Biometrics, vol. 46, no. 1, pp. 33–48, 1990.
[9]  J. Babb, A. Rogatko, and S. Zacks, “Cancer phase I clinical trials: efficient dose escalation with overdose control,” Statistics in Medicine, vol. 17, no. 10, pp. 1103–1120, 1998.
[10]  E. L. Korn, D. Midthune, T. Timothy Chen, et al., “A comparison of two phase I trial designs,” Statistics in Medicine, vol. 13, no. 18, pp. 1799–1806, 1994.
[11]  D. Faries, “Practical modifications of the continual reassessment method for phase I cancer clinical trials,” Journal of Biopharmaceutical Statistics, vol. 4, no. 2, pp. 147–164, 1994.
[12]  S. Zacks, A. Rogatko, and J. Babb, “Optimal Bayesian-feasible dose escalation for cancer phase I trials,” Statistics & Probability Letters, vol. 38, no. 3, pp. 215–220, 1998.
[13]  R. Mugno, W. Zhus, and W. F. Rosenberger, “Adaptive urn designs for estimating several percentiles of a dose-response curve,” Statistics in Medicine, vol. 23, no. 13, pp. 2137–2150, 2004.
[14]  Z. Chen, M. D. Krailo, J. Sun, and S. P. Azen, “Range and trend of expected toxicity level (ETL) in standard A?+?B designs: a report from the children's oncology group,” Contemporary Clinical Trials, vol. 30, no. 2, pp. 123–128, 2009.
[15]  Z. Chen, M. D. Krailo, S. P. Azen, and M. Tighiouart, “A novel toxicity scoring system treating toxicity response as a quasi-continuous variable in phase I clinical trials,” Contemporary Clinical Trials, vol. 31, no. 5, pp. 473–482, 2010.
[16]  Z. Chen, M. Tighiouart, and J. Kowalski, “Dose escalation with overdose control using a quasi-continuous toxicity score in cancer phase I clinical trials,” Contemporary Clinical Trials, vol. 33, no. 5, pp. 949–958, 2012.
[17]  M. Tighiouart and A. Rogatko, “Dose finding with escalation with overdose control (EWOC) in cancer clinical trials,” Statistical Science, vol. 25, no. 2, pp. 217–226, 2010.
[18]  W. F. Rosenberger and L. M. Haines, “Competing designs for phase I clinical trials: a review,” Statistics in Medicine, vol. 21, no. 18, pp. 2757–2770, 2002.
[19]  C. Le Tourneau, J. J. Lee, and L. L. Siu, “Dose escalation methods in phase I cancer clinical trials,” Journal of the National Cancer Institute, vol. 101, no. 10, pp. 708–720, 2009.
[20]  E. A. Gehan, “The determination of the number of patients required in a preliminary and a follow-up trial of a new chemotherapeutic agent,” Journal of Chronic Diseases, vol. 13, no. 4, pp. 346–353, 1961.
[21]  R. Simon, “Optimal two-stage designs for phase II clinical trials,” Controlled Clinical Trials, vol. 10, no. 1, pp. 1–10, 1989.
[22]  T. R. Fleming, “One-sample multiple testing procedure for phase II clinical trials,” Biometrics, vol. 38, no. 1, pp. 143–151, 1982.
[23]  M. N. Chang, T. M. Therneau, H. S. Wieand, and S. S. Cha, “Design for group sequential phase II clinical trials,” Biometrics, vol. 43, no. 4, pp. 865–874, 1987.
[24]  P. F. Thall and R. Simon, “A Bayesian approach to establishing sample size and monitoring criteria for phase II clinical trials,” Controlled Clinical Trials, vol. 15, no. 6, pp. 463–481, 1994.
[25]  J. J. Lee and D. D. Liu, “A predictive probability design for phase II cancer clinical trials,” Clinical Trials, vol. 5, no. 2, pp. 93–106, 2008.
[26]  G. Yin, N. Chen, and J. J. Lee, “Phase II trial design with Bayesian adaptive randomization and predictive probability,” Journal of the Royal Statistical Society C, vol. 61, no. 2, pp. 219–235, 2012.
[27]  L. Rubinstein, J. Crowley, P. Ivy, M. Leblanc, and D. Sargent, “Randomized phase II designs,” Clinical Cancer Research, vol. 15, no. 6, pp. 1883–1890, 2009.
[28]  P. F. Thall, “A review of phase 2-3 clinical trial designs,” Lifetime Data Analysis, vol. 14, no. 1, pp. 37–53, 2008.
[29]  L. Y. T. Inoue, P. F. Thall, and D. A. Berry, “Seamlessly expanding a randomized phase II trial to phase III,” Biometrics, vol. 58, no. 4, pp. 823–831, 2002.
[30]  P. T. Lavin, “An alternative model for the evaluation of antitumor activity,” Cancer Clinical Trials, vol. 4, no. 4, pp. 451–457, 1981.
[31]  Y. Wang, C. Sung, C. Dartois et al., “Elucidation of relationship between tumor size and survival in non-small-cell lung cancer patients can aid early decision making in clinical drug development,” Clinical Pharmacology and Therapeutics, vol. 86, no. 2, pp. 167–174, 2009.
[32]  T. G. Karrison, M. L. Maitland, W. M. Stadler, et al., “Design of phase II cancer trials using a continuous endpoint of change in tumor size: application to a study of sorafenib and erlotinib in non-small-cell lung cancer,” Journal of the National Cancer Institute, vol. 99, no. 19, pp. 1455–1461, 2007.
[33]  M. W. An, S. J. Mandrekar, M. E. Branda, et al., “Comparison of continuous versus categorical tumor measurement–based metrics to predict overall survival in cancer treatment trials,” Clinical Cancer Research, vol. 17, no. 20, pp. 6592–6599, 2011.
[34]  G. Yothers, “Toward progression-free survival as a primary end point in advanced colorectal cancer,” Journal of Clinical Oncology, vol. 25, no. 33, pp. 5153–5154, 2007.
[35]  M. Buyse, P. Thirion, R. W. Carlson, T. Burzykowski, G. Molenberghs, and P. Piedbois, “Relation between tumour response to first-line chemotherapy and survival in advanced colorectal cancer: a meta-analysis. Meta-Analysis Group in Cancer,” The Lancet, vol. 356, no. 9227, pp. 373–378, 2000.
[36]  K. F. Schulz and D. A. Grimes, “Generation of allocation sequences in randomised trials: chance, not choice,” The Lancet, vol. 359, no. 9305, pp. 515–519, 2002.
[37]  J. M. Lachin, J. P. Matts, and L. J. Wei, “Randomization in clinical trials: conclusions and recommendations,” Controlled Clinical Trials, vol. 9, no. 4, pp. 365–374, 1988.
[38]  D. R. Taves, “Minimization: a new method of assigning patients to treatment and control groups,” Clinical Pharmacology and Therapeutics, vol. 15, no. 5, pp. 443–453, 1974.
[39]  S. J. Pocock and R. Simon, “Sequential treatment assignment with balancing for prognostic factors in the controlled clinical trial,” Biometrics, vol. 31, no. 1, pp. 103–115, 1975.
[40]  J. W. Frane, “A method of biased coin randomization, its implementation, and its validation,” Drug Information Journal, vol. 32, no. 2, pp. 423–432, 1998.
[41]  M. Kang, B. G. Ragan, and J. H. Park, “Issues in outcomes research: an overview of randomization techniques for clinical trials,” Journal of Athletic Training, vol. 43, no. 2, pp. 215–221, 2008.
[42]  S. J. Pocock, “Group sequential methods in the design and analysis of clinical trials,” Biometrika, vol. 64, no. 2, pp. 191–199, 1977.
[43]  P. C. O'Brien and T. R. Fleming, “A multiple testing procedure for clinical trials,” Biometrics, vol. 35, no. 3, pp. 549–556, 1979.
[44]  D. L. DeMets and K. K. G. Lan, “Interim analysis: the alpha spending function approach,” Statistics in Medicine, vol. 13, no. 13-14, pp. 1341–1352, 1994.
[45]  I. K. Hwang, W. J. Shih, and J. S. Decani, “Group sequential designs using a family of type I error probability spending functions,” Statistics in Medicine, vol. 9, no. 12, pp. 1439–1445, 1990.
[46]  C. Jennison and B. W. Turnbull, Group Sequential Methods with Applications to Clinical Trials, CRC Press, 2000.
[47]  S. M. Berry and J. B. Kadane, “Optimal Bayesian randomization,” Journal of the Royal Statistical Society B, vol. 59, no. 4, pp. 813–819, 1997.
[48]  P. F. Thall and J. K. Wathen, “Practical Bayesian adaptive randomisation in clinical trials,” European Journal of Cancer, vol. 43, no. 5, pp. 859–866, 2007.
[49]  G. L. Rosner, “Bayesian monitoring of clinical trials with failure-time endpoints,” Biometrics, vol. 61, no. 1, pp. 239–245, 2005.
[50]  A. Gelman, J. B. Carlin, H. S. Stern, and D. B. Rubin, Bayesian Data Analysis, Chapman & Hall, Boca Raton, Fla, USA, 1995.
[51]  D. A. Berry and C. H. Ho, “One-sided sequential stopping boundaries for clinical trials: a decision-theoretic approach,” Biometrics, vol. 44, no. 1, pp. 219–227, 1988.
[52]  J. D. Eales and C. Jennison, “An improved method for deriving optimal one-sided group sequential tests,” Biometrika, vol. 79, no. 1, pp. 13–24, 1992.
[53]  N. Cressie and J. Biele, “A sample-size-optimal Bayesian procedure for sequential pharmaceutical trials,” Biometrics, vol. 50, no. 3, pp. 700–711, 1994.
[54]  S. Barber and C. Jennison, “Optimal asymmetric one-sided group sequential tests,” Biometrika, vol. 89, no. 1, pp. 49–60, 2002.
[55]  M. Krams, K. R. Lees, W. Hacke, A. P. Grieve, J. M. Orgogozo, and G. A. Ford, “Acute stroke therapy by inhibition of neutrophils (ASTIN): an adaptive dose-response study of UK-279,276 in acute ischemic stroke,” Stroke, vol. 34, no. 11, pp. 2543–2548, 2003.
[56]  B. R. Luce, J. M. Kramer, S. N. Goodman et al., “Rethinking randomized clinical trials for comparative effectiveness research: the need for transformational change,” Annals of Internal Medicine, vol. 151, no. 3, pp. 206–209, 2009.
[57]  M. Posch and P. Bauer, “Adaptive two stage designs and the conditional error function,” Biometrical Journal, vol. 41, no. 6, pp. 689–696, 1999.
[58]  L. Cui, H. M. J. Hung, and S. J. Wang, “Modification of sample size in group sequential clinical trials,” Biometrics, vol. 55, no. 3, pp. 853–857, 1999.
[59]  W. J. Shih, “Group sequential, sample size re-estimation and two-stage adaptive designs in clinical trials: a comparison,” Statistics in Medicine, vol. 25, no. 6, pp. 933–941, 2006.
[60]  M. A. Proschan and S. A. Hunsberger, “Designed extension of studies based on conditional power,” Biometrics, vol. 51, no. 4, pp. 1315–1324, 1995.
[61]  M. A. Proschan, “Two-stage sample size re-estimation based on a nuisance parameter: a review,” Journal of Biopharmaceutical Statistics, vol. 15, no. 4, pp. 559–574, 2005.

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