Statistical Science

Bayesian Models and Decision Algorithms for Complex Early Phase Clinical Trials

Peter F. Thall

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An early phase clinical trial is the first step in evaluating the effects in humans of a potential new anti-disease agent or combination of agents. Usually called “phase I” or “phase I/II” trials, these experiments typically have the nominal scientific goal of determining an acceptable dose, most often based on adverse event probabilities. This arose from a tradition of phase I trials to evaluate cytotoxic agents for treating cancer, although some methods may be applied in other medical settings, such as treatment of stroke or immunological diseases. Most modern statistical designs for early phase trials include model-based, outcome-adaptive decision rules that choose doses for successive patient cohorts based on data from previous patients in the trial. Such designs have seen limited use in clinical practice, however, due to their complexity, the requirement of intensive, computer-based data monitoring, and the medical community’s resistance to change. Still, many actual applications of model-based outcome-adaptive designs have been remarkably successful in terms of both patient benefit and scientific outcome. In this paper I will review several Bayesian early phase trial designs that were tailored to accommodate specific complexities of the treatment regime and patient outcomes in particular clinical settings.

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Statist. Sci., Volume 25, Number 2 (2010), 227-244.

First available in Project Euclid: 19 November 2010

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Adaptive design Bayesian design clinical trial dose-finding phase I trial phase I/II trial


Thall, Peter F. Bayesian Models and Decision Algorithms for Complex Early Phase Clinical Trials. Statist. Sci. 25 (2010), no. 2, 227--244. doi:10.1214/09-STS315.

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  • Albert, J. H. and Chib, S. (1993). Bayesian analysis of binary and polytomous response data. J. Amer. Statist. Assoc. 88 669–679.
  • Andersson, B. S., Thall, P. F., Madden, T., Couriel, D., Wang, X., Tran, H. T., Anderlini, P., de Lima, M., Gajewski, J. and Champlin, R. E. (2002). Busulfan systemic exposure relative to regimen-related toxicity and acute graft vs. host disease; defining a therapeutic window for IVBuCy2 in chronic myelogenous leukemia. Biology of Blood and Marrow Transplantation 8 477–485.
  • Babb, J. S. and Rogatko, A. (2001). Patient specific dosing in a phase I cancer trial. Stat. Med. 20 2079–2090.
  • Bekele, B. N. and Thall, P. F. (2004). Dose-finding based on multiple toxicities in a soft tissue sarcoma trial. J. Amer. Statist. Assoc. 99 26–35.
  • Bekele, B. N., Ji, Y., Shen, Y. and Thall, P. F. (2008). Monitoring late onset toxicities in phase I trials using predicted risks. Biostatistics 9 442–457.
  • Braun, T. (2002). The bivariate continual reassessment method: Extending the CRM to phase I trials of two competing outcomes. Controlled Clinical Trials 23 240–256.
  • Braun, T. M., Yuan, Z. and Thall, P. F. (2005). Determining a maximum tolerated schedule of a cytotoxic agent. Biometrics 61 335–343.
  • Braun, T. M., Thall, P. F., Nguyen, H. and de Lima, M. (2007). Simultaneously optimizing dose and schedule of a new cytotoxic agent. Clinical Trials 4 113–124.
  • Cheung, Y. K. (2005). Coherence principles in dose-finding studies. Biometrika 92 863–873.
  • Cheung, Y. and Chappell, R. (2000). Sequential designs for phase I clinical trials with late-onset toxicities. Biometrics 56 1177–1182.
  • Dette, H., Bretz, F., Pepelyshev, A. and Pinhiero, J. (2008). Optimal designs for dose-finding studies. J. Amer. Statist. Assoc. 103 1225–1237.
  • Goodman, S. G., Zahurak, M. L. and Piantadosi, S. (1995). Some practical improvements in the continual reassessment method. Stat. Med. 14 1149–1161.
  • Gooley, T. A., Martin, P. J., Fisher, L. D. and Pettinger, M. (1994). Simulation as a design tool for phase I/II clinical trials: An example from bone marrow transplantation. Controlled Clinical Trials 15 450–462.
  • Haines, L. M., Perevozskaya, I. and Rosenberger, W. F. (2003). Bayesian optimal designs for phase I clinical trials. Biometrics 59 591–600.
  • Horstmann, E., McCabe, M. S., Grochow, L., Yamamoto, S., Rubinstein, L., Budd, T., Shoemaker, D., Emanuel, E. J. and Grady, C. (2005). Risks and benefits of phase 1 oncology trials, 1991 through 2002. New England J. Medicine 352 895–904.
  • Houede, N., Thall, P. F., Nguyen, H., Paoletti, X. and Kramar, A. (2010). Utility-based optimization of combination therapy using ordinal toxicity and efficacy in phase I/II trials. Biometrics. In press.
  • Ivanova, A. (2003). A new dose-finding design for bivariate outcomes. Biometrics 59 1001–1007.
  • Liu, C. A. and Braun, T. M. (2009). Parametric non-mixture cure models for schedule-finding of therapeutic agents. Appl. Statist. 58 225–236.
  • Morita, S., Thall, P. F. and Müller, P. (2008). Determining the effective sample size of a parametric prior. Biometrics 64 595–602.
  • O’Quigley, J. (1990). Sequential design and analysis of dose-finding studies in patients with life threatening disease. Fund. Clin. Pharmacology 4 (Suppl. 2) 81s–91s.
  • O’Quigley, J., Hughes, M. D. and Fenton, T. (2001). Dose-finding designs for HIV studies. Biometrics 57 1018–1029.
  • O’Quigley, J., Pepe, M. and Fisher, L. (1990). Continual reassessment method: A practical design for phase I clinical trials in cancer. Biometrics 46 33–48.
  • Palmer, C. R. (2002). Ethics, data-dependent designs, and the strategy of clinical trials: Time to start learning-as-we-go? Stat. Methods Med. Res. 5 381–402.
  • Ratain, M. J., Mick, R., Janisch, L., Berezin, F., Schilsky, R. L., Vogelzang, N. J. and Kut, M. (1996). Individualized dosing of amonafide based on a pharmacodynamic model incorporating acetylator phenotype and gender. Pharmacogenetics 6 93–101.
  • Thall, P. F. and Cook, J. D. (2004). Dose-finding based on efficacy-toxicity trade-offs. Biometrics 60 684–693.
  • Thall, P. F. and Russell, K. T. (1998). A strategy for dose finding and safety monitoring based on efficacy and adverse outcomes in phase I/II clinical trials. Biometrics 54 251– 264.
  • Thall, P. F., Cook, J. D. and Estey, E. H. (2006). Adaptive dose selection using efficacy-toxicity trade-offs: Illustrations and practical considerations. J. Biopharm. Statist. 16 623–638.
  • Thall, P. F., Nguyen, H. and Estey, E. H. (2008). Patient-specific dose-finding based on bivariate outcomes and covariates. Biometrics 64 1126–1136.
  • Thall, P. F., Lee, J. J., Tseng, C.-H. and Estey, E. H. (1999). Accrual strategies for phase I trials with delayed patient outcome. Stat. Med. 18 1155–1169.
  • Thall, P. F., Millikan, R. E., Müller, P. and Lee, S.-J. (2003). Dose-finding with two agents in phase I oncology trials. Biometrics 59 487–496.
  • Thall, P. F., Simon, R. and Estey, E. H. (1995). Bayesian sequential monitoring designs for single-arm clinical trials with multiple outcomes. Stat. Med. 14 357–379.
  • Tukey, J. W. (1949). One degree of freedom for non-additivity. Biometrics 5 232–242.
  • Whelan, H. T., Cook, J. D., Amlie-Lefond, C. M., Hovinga, C. A., Chan, A. K., Ichord, R. N., deVeber, G. A. and Thall, P. F. (2008). Practical model-based dose-finding in early phase clinical trials: Optimizing tissue plasminogen activator dose for treatment of ischemic stroke in children. Stroke 39 2627–2636.