Adaptive dose modification for phase I clinical trials

Most phase I dose-finding methods in oncology aim to find the maximum-tolerated dose from a set of prespecified doses. However, in practice, because of a lack of understanding of the true dose-toxicity relationship, it is likely that none of these prespecified doses are equal or reasonably close to the true maximum-tolerated dose. To handle this issue, we propose an adaptive dose modification (ADM) method that can be coupled with any existing dose-finding method to adaptively modify the dose, when it is needed, during the course of dose finding. To reflect clinical practice, we divide the toxicity probability into three regions: underdosing, acceptable, and overdosing regions. We adaptively add a new dose whenever the observed data suggest that none of the investigational doses are likely to be located in the acceptable region. The new dose is estimated via a nonparametric dose-toxicity model based on local polynomial regression. The simulation study shows that ADM substantially outperforms the similar existing method. We applied ADM to a phase I cancer trial. Copyright © 2016 John Wiley & Sons, Ltd.

[1]  Ying Yuan,et al.  Bayesian hybrid dose‐finding design in phase I oncology clinical trials , 2011, Statistics in medicine.

[2]  M. Wand,et al.  Multivariate Locally Weighted Least Squares Regression , 1994 .

[3]  B E Storer,et al.  Design and analysis of phase I clinical trials. , 1989, Biometrics.

[4]  Yuan Ji,et al.  A modified toxicity probability interval method for dose-finding trials. , 2010, Clinical trials.

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

[6]  G. Yin,et al.  Dose–Response Curve Estimation: A Semiparametric Mixture Approach , 2011, Biometrics.

[7]  D H Leung,et al.  Isotonic designs for phase I trials. , 2001, Controlled clinical trials.

[8]  Adaptive dose insertion in early phase clinical trials , 2013, Clinical trials.

[9]  Hans-Georg Müller,et al.  Kernel and Probit Estimates in Quantal Bioassay , 1988 .

[10]  Jianqing Fan,et al.  Variable Bandwidth and Local Linear Regression Smoothers , 1992 .

[11]  T. Fojo,et al.  Phase I trial and pharmacokinetic study of ixabepilone administered daily for 5 days in children and adolescents with refractory solid tumors. , 2009, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[12]  John O'Quigley,et al.  Consistency of continual reassessment method under model misspecification , 1996 .

[13]  Jianqing Fan,et al.  On automatic boundary corrections , 1997 .

[14]  Ying Kuen Cheung,et al.  On the Use of Nonparametric Curves in Phase I Trials with Low Toxicity Tolerance , 2002, Biometrics.

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

[16]  E. Mammen,et al.  A General Projection Framework for Constrained Smoothing , 2001 .

[17]  Ying Yuan,et al.  Bayesian optimal interval designs for phase I clinical trials , 2015, Journal of the Royal Statistical Society: Series C (Applied Statistics).