Project Euclid

References

• Babb, J., Rogatko, A. and Zacks, S. (1998). Cancer Phase I clinical trials: Efficient dose escalation with overdose control. Statist. Med. 17 1103–1120.
• 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.
• Cheung, Y.-K. (2002). On the use of nonparametric curves in phase I trials with low toxicity tolerance. Biometrics 58 237–240.
  Mathematical Reviews (MathSciNet): MR1891385
  Digital Object Identifier: doi:10.1111/j.0006-341X.2002.00237.x
  JSTOR: links.jstor.org
  
• Cheung, Y.-K. (2005). Coherence principles in dose-finding studies. Biometrika 92 863–873.
  Mathematical Reviews (MathSciNet): MR2234191
  Zentralblatt MATH: 1151.62353
  Digital Object Identifier: doi:10.1093/biomet/92.4.863
  
• Cheung, Y.-K. and Chappell, R. (2002). A simple technique to evaluate model sensitivity in the continual reassessment method. Biometrics 58 671–674.
  Mathematical Reviews (MathSciNet): MR1933538
  Digital Object Identifier: doi:10.1111/j.0006-341X.2002.00671.x
  JSTOR: links.jstor.org
  
• Cheung, Y.-K. and Elkind, M. S. (2010). Stochastic approximation with virtual observations for dose-finding on discrete levels. Biometrika 97 109–121.
• Chevret, S. (1993). The continual reassessment method in cancer phase I clinical trials: A simulation study. Stat. Med. 12 1093–1108.
• Faries, D. (2004). Practical modifications of the continual reassessment method for phase I cancer clinical trials. J. Biopharm. Statist. 4 147–164.
• Gatsonis, C. and Greenhouse, J. B. (1992). Bayesian methods for phase I clnical trials. Stat. Med. 11 1377–1389.
• Gasparini, M. and Eisele, J. (2000). A curve free method for Phase I clinical trials. Biometrics 56 609–615.
  Mathematical Reviews (MathSciNet): MR1795024
  Digital Object Identifier: doi:10.1111/j.0006-341X.2000.00609.x
  JSTOR: links.jstor.org
  
• Gerke, O. and Siedentop, H. (2008). Optimal Phase I dose-escalation trial designs in oncology—A simulation study. Stat. Med. 27 5329–5344.
• Goodman, S., Zahurak, M. L. and Piantadosi, S. (1995). Some practical improvements in the continual reassessment method for phase I studies. Stat. Med. 14 1149–1161.
• Huber, P. J. (1967). The behavior of maximum likelihood estimates under nonstandard conditions. In Proc. 5th Berkeley Sympos. Math. Statist. Probab. 1 221–233. Univ. California Press, Berkeley, CA.
  Mathematical Reviews (MathSciNet): MR216620
  Zentralblatt MATH: 0212.21504
  
• Iasonos, A., Wilton, A., Riedel, E., Seshan, V. and Spriggs, D. (2008). A comprehensive comparison of the continual reassessment method to the standard 3+3 dose escalation scheme in Phase I dose-finding studies. Clinical Trials 5 465–477.
• Ishizuka, N. and Ohashi, Y. (2001). The continual reassessment method and its applications: A Bayesian methodology for phase I cancer clinical trials. Stat. Med. 20 2661–2681.
• Lee, S. and Cheung, Y.-K. (2009). Model calibration in the continual reassessment method. Clinical Trials 6 227–238.
• Legedeza, A. and Ibrahim, J. (2002). Longitudinal design for phase I trials using the continual reassessment method. Controlled Clinical Trials 21 578–588.
• Mahmood, I. (2001). Application of preclinical data to initiate the modified continual reassessment method for maximum tolerated dose-finding trial. J. Clin. Pharmacol. 41 19–24.
• Martz, H. F. and Waller, R. A. (1982). Bayesian Reliability Analysis. Wiley, New York.
  Mathematical Reviews (MathSciNet): MR670595
  
• Moller, S. (1995). An extension of the continual reassessment method using a preliminary up and down design in a dose finding study in cancer patients in order to investigate a greater number of dose levels. Stat. Med. 14 911–922.
• O’Quigley, J. (1992). Estimating the probability of toxicity at the recommended dose following a Phase I clinical trial in cancer. Biometrics 48 853–862.
• O’Quigley, J. (1999). Another look at two Phase I clinical trial designs (with commentary). Stat. Med. 18 2683–2692.
• O’Quigley, J. (2002a). Continual reassessment designs with early termination. Biostatistics 3 87–99.
• O’Quigley, J. (2002b). Curve free and model based CRM designs. Biometrics 58 77–81.
• O’Quigley, J. (2006). Theoretical study of the continual reassessment method. J. Statist. Plann. Inference 136 1765–1780.
  Mathematical Reviews (MathSciNet): MR2255595
  Zentralblatt MATH: 05030770
  Digital Object Identifier: doi:10.1016/j.jspi.2005.08.003
  
• O’Quigley, J. and Chevret, S. (1991). Methods for dose finding studies in cancer clinical trials: A review and results of a Monte Carlo study. Stat. Med. 10 1647–1664.
• O’Quigley, J., Hughes, M. and Fenton, T. (2001). Dose finding designs for HIV studies. Biometrics 57 1018–1029.
  Mathematical Reviews (MathSciNet): MR1973811
  Digital Object Identifier: doi:10.1111/j.0006-341X.2001.01018.x
  JSTOR: links.jstor.org
  
• O’Quigley, J. and Paoletti, X. (2003). Continual reassessment method for ordered groups. Biometrics 59 429–439.
  Mathematical Reviews (MathSciNet): MR1987410
  Digital Object Identifier: doi:10.1111/1541-0420.00050
  JSTOR: links.jstor.org
  
• O’Quigley, J. and Reiner, E. (1998). A stopping rule for the continual reassessment method. Biometrika 85 741–748.
  Mathematical Reviews (MathSciNet): MR1665850
  Zentralblatt MATH: 0918.62086
  Digital Object Identifier: doi:10.1093/biomet/85.3.741
  JSTOR: links.jstor.org
  
• O’Quigley, J. and Shen, L. Z. (1996). Continual reassessment method: A likelihood approach. Biometrics 52 163–174.
• O’Quigley, J., Paoletti, X. and Maccario, J. (2002). Non-parametric optimal design in dose finding studies. Biostatistics 3 51–56.
• 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.
• O’Quigley, J., Shen, L. and Gamst, A. (1999). Two sample continual reassessment method. J. Biopharm. Statist. 9 17–44.
• Paoletti, X., O’Quigley, J. and Maccario, J. (2004). Design efficiency in dose finding studies. Comput. Statist. Data Anal. 45 197–214.
  Mathematical Reviews (MathSciNet): MR2045468
  
• Piantadosi, S. and Liu, G. (1996). Improved designs for dose escalation studies using pharmacokinetic measurements. Stat. Med. 15 1605–1618.
• Shen, L. Z. and O’Quigley, J. (1996). Consistency of continual reassessment method in dose finding studies. Biometrika 83 395–406.
• Shen, L. Z. and O’Quigley, J. (2000). Using a one-parameter model to sequentially estimate the root of a regression function. Comput. Statist. Data Anal. 34 357–369.
  Mathematical Reviews (MathSciNet): MR1792407
  
• Shu, J. and O’Quigley, J. (2008). Dose escalation designs in oncology: ADEPT and the CRM. Stat. Med. 27 5345–5353.
• Silvapulle, M. J. (1981). On the existence of maximum likelihood estimators for the binomial response models. J. R. Stat. Soc. B Stat. Methodol. 43 310–313.
  Mathematical Reviews (MathSciNet): MR637943
  JSTOR: links.jstor.org
  
• Storer, B. E. (1989). Design and analysis of Phase I clinical trials. Biometrics 45 925–937.
  Mathematical Reviews (MathSciNet): MR1029610
  Digital Object Identifier: doi:10.2307/2531693
  JSTOR: links.jstor.org
  
• Tighiouart, M., Rogatko, A. and Babb, J. (2005). Flexible Bayesian methods for cancer phase I clinical trials: Dose escalation with overdose control. Stat. Med. 24 2183–2196.
  Mathematical Reviews (MathSciNet): MR2146926
  Digital Object Identifier: doi:10.1002/sim.2106
  
• Whitehead, J. and Brunier, H. (1995). Bayesian decision procedures for dose determining experiments. Stat. Med. 14 885–893.
• Whitehead, J. and Williamson, D. (1998). Bayesian decision procedures based on logistic regression models for dose-finding studies. J. Biopharm. Statist. 8 445–467.
• Whitehead, J., Thygesen, H. and Whitehead, A. (2010). A Bayesian dose-finding procedure for phase I clinical trials based only on the assumption of monotonicity. Stat. Med. 29 1808–1824.
• Wu, C. F. J. (1985). Efficient sequential designs with binary data. J. Amer. Statist. Assoc. 80 974–984.
  Mathematical Reviews (MathSciNet): MR819603
  Zentralblatt MATH: 0588.62133
  Digital Object Identifier: doi:10.2307/2288563
  JSTOR: links.jstor.org
  
• Wu, C. F. J. (1986). Maximum likelihood recursion and stochastic approximation in sequential designs. Adaptive Statistical Procedures and Related Topics (J. van Ryzin, ed.). Institute of Mathematical Statistics Monograph 8 298–313. IMS, Hayward, CA.
  Mathematical Reviews (MathSciNet): MR898255
  Zentralblatt MATH: 0679.62066
  Digital Object Identifier: doi:10.1214/lnms/1215540307
  
• Yin, G. and Yuan, Y. (2009). Bayesian model averaging continual reassessment method in Phase I clinical trials. J. Amer. Statist. Assoc. 104 954–968.
• Zohar, S. and O’Quigley, J. (2006a). Identifying the most successful dose (MSD) in dose-finding studies in cancer. Pharmaceutical Statistics 5 187–199.
• Zohar, S. and O’Quigley, J. (2006b). Optimal designs for identifying the most successful dose. Stat. Med. 25 4311–4320.