Incorporating Individual and Collective Ethics into Phase I Cancer Trial Designs

A general framework is proposed for Bayesian model based designs of Phase I cancer trials, in which a general criterion for coherence (Cheung, 2005, Biometrika 92, 863-873) of a design is also developed. This framework can incorporate both "individual" and "collective" ethics into the design of the trial. We propose a new design that minimizes a risk function composed of two terms, with one representing the individual risk of the current dose and the other representing the collective risk. The performance of this design, which is measured in terms of the accuracy of the estimated target dose at the end of the trial, the toxicity and overdose rates, and certain loss functions reflecting the individual and collective ethics, is studied and compared with existing Bayesian model based designs and is shown to have better performance than existing designs.

[1]  Mourad Tighiouart,et al.  Translation of innovative designs into phase I trials. , 2007, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[2]  S Zacks,et al.  Cancer phase I clinical trials: efficient dose escalation with overdose control. , 1998, Statistics in medicine.

[3]  J O'Quigley,et al.  Continual reassessment method: a practical design for phase 1 clinical trials in cancer. , 1990, Biometrics.

[4]  Nancy Flournoy,et al.  An adaptive design for maximization of a contingent binary response , 1995 .

[5]  W. J. Studden,et al.  Theory Of Optimal Experiments , 1972 .

[6]  James S. Babb and Andre´ Rogatko Bayesian Methods for Cancer Phase I Clinical Trials , 2003 .

[7]  Valerii V. Fedorov,et al.  Adaptive designs for selecting drug combinations based on efficacy–toxicity response , 2008 .

[8]  Q. Stout,et al.  Optimizing a Unimodal Response Function for Binary Variables , 2001 .

[9]  Luc Pronzato,et al.  Penalized optimal designs for dose-finding , 2010 .

[10]  N. Flournoy,et al.  An Optimizing Up-and-Down Design , 2001 .

[11]  L. Haines,et al.  Bayesian Optimal Designs for Phase I Clinical Trials , 2003, Biometrics.

[12]  P. Thall,et al.  Dose‐Finding Based on Efficacy–Toxicity Trade‐Offs , 2004, Biometrics.

[13]  V. Fedorov,et al.  Adaptive designs for dose-finding based on efficacy–toxicity response , 2006 .

[14]  Shelemyahu Zacks,et al.  Optimal Bayesian-feasible dose escalation for cancer phase I trials , 1998 .

[15]  D. D. Hoff,et al.  Response rates, duration of response, and dose response effects in phase I studies of antineoplastics , 1991, Investigational New Drugs.

[16]  Jay Bartroff,et al.  Approximate Dynamic Programming and Its Applications to the Design of Phase I Cancer Trials , 2010, 1011.6509.

[17]  W. Näther Optimum experimental designs , 1994 .

[18]  William F Rosenberger,et al.  Competing designs for phase I clinical trials: a review , 2002, Statistics in medicine.

[19]  Dimitri P. Bertsekas,et al.  Dynamic Programming and Optimal Control, Two Volume Set , 1995 .

[20]  Ying Kuen Cheung,et al.  Coherence principles in dose-finding studies , 2005 .

[21]  Holger Dette,et al.  Optimal designs for a class of nonlinear regression models , 2002 .

[22]  丸山 徹 Convex Analysisの二,三の進展について , 1977 .

[23]  N. Geller Advances in Clinical Trial Biostatistics , 2003 .

[24]  James S. Babb and Andre´ Rogatko Bayesian Methods for Cancer Phase I Clinical Trials , 2003 .

[25]  Hoon Kim,et al.  Monte Carlo Statistical Methods , 2000, Technometrics.

[26]  P. Laycock,et al.  Optimum Experimental Designs , 1995 .

[27]  J Whitehead,et al.  Bayesian decision procedures for dose determining experiments. , 1995, Statistics in medicine.