An information theoretic phase I–II design for molecularly targeted agents that does not require an assumption of monotonicity

Summary For many years phase I and phase II clinical trials have been conducted separately, but there has been a recent shift to combine these phases. Although a variety of phase I–II model‐based designs for cytotoxic agents have been proposed in the literature, methods for molecularly targeted agents (TAs) are just starting to develop. The main challenge of the TA setting is the unknown dose–efficacy relationship that can have either an increasing, plateau or umbrella shape. To capture these, approaches with more parameters are needed or, alternatively, more orderings are required to account for the uncertainty in the dose–efficacy relationship. As a result, designs for more complex clinical trials, e.g. trials looking at schedules of a combination treatment involving TAs, have not been extensively studied yet. We propose a novel regimen finding design which is based on a derived efficacy–toxicity trade‐off function. Because of its special properties, an accurate regimen selection can be achieved without any parametric or monotonicity assumptions. We illustrate how this design can be applied in the context of a complex combination–schedule clinical trial. We discuss practical and ethical issues such as coherence, delayed and missing efficacy responses, safety and futility constraints.

[1]  T Jaki,et al.  Loss functions in restricted parameter spaces and their Bayesian applications , 2017, Journal of applied statistics.

[2]  P F Thall,et al.  A strategy for dose-finding and safety monitoring based on efficacy and adverse outcomes in phase I/II clinical trials. , 1998, Biometrics.

[3]  Robert F. Tate,et al.  THE THEORY OF CORRELATION BETWEEN TWO CONTINUOUS VARIABLES WHEN ONE IS DICHOTOMIZED , 1955 .

[4]  John O'Quigley,et al.  Continual Reassessment Method for Partial Ordering , 2011, Biometrics.

[5]  Adriana Clim,et al.  Weighted entropy with application , 2008 .

[6]  Claude Emond,et al.  Non-monotonic dose-response relationships and endocrine disruptors: a qualitative method of assessment , 2015, Environmental Health.

[7]  Ying Yuan,et al.  A Bayesian dose finding design for oncology clinical trials of combinational biological agents , 2014, Journal of the Royal Statistical Society. Series C, Applied statistics.

[8]  Ying Yuan,et al.  Phase I/II dose-finding design for molecularly targeted agent: Plateau determination using adaptive randomization , 2018, Statistical methods in medical research.

[9]  R. Conolly,et al.  Nonmonotonic dose-response relationships: mechanistic basis, kinetic modeling, and implications for risk assessment. , 2004, Toxicological sciences : an official journal of the Society of Toxicology.

[10]  Eva Steliarova-Foucher,et al.  International incidence of childhood cancer, 2001–10: a population-based registry study , 2017, The Lancet. Oncology.

[11]  Xavier Paoletti,et al.  Phase I trials of molecularly targeted agents: should we pay more attention to late toxicities? , 2011, Journal of clinical oncology : official journal of the American Society of Clinical Oncology.

[12]  Silviu Guiasu,et al.  A quantitative-qualitative measure of information in cybernetic systems (Corresp.) , 1968, IEEE Trans. Inf. Theory.

[13]  G. Mateu-Figueras,et al.  The normal distribution in some constrained sample spaces , 2008, 0802.2643.

[14]  T A Gooley,et al.  Simulation as a design tool for phase I/II clinical trials: an example from bone marrow transplantation. , 1994, Controlled clinical trials.

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

[16]  Ying Yuan,et al.  Adaptive designs for identifying optimal biological dose for molecularly targeted agents , 2014, Clinical trials.

[17]  Antoni Ribas,et al.  Anti-programmed-death-receptor-1 treatment with pembrolizumab in ipilimumab-refractory advanced melanoma: a randomised dose-comparison cohort of a phase 1 trial , 2014, The Lancet.

[18]  P. Thall,et al.  Practical Bayesian adaptive randomisation in clinical trials. , 2007, European journal of cancer.

[19]  Ying Kuen Cheung,et al.  Two‐Stage Designs for Dose‐Finding Trials with a Biologic Endpoint Using Stepwise Tests , 2008, Biometrics.

[20]  Thomas M. Cover,et al.  Elements of Information Theory , 2005 .

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

[22]  John M Maris,et al.  Purged versus non-purged peripheral blood stem-cell transplantation for high-risk neuroblastoma (COG A3973): a randomised phase 3 trial. , 2013, The Lancet. Oncology.

[23]  Nolan A Wages,et al.  Seamless Phase I/II Adaptive Design for Oncology Trials of Molecularly Targeted Agents , 2015, Journal of biopharmaceutical statistics.

[24]  Thomas Jaki,et al.  Bayesian adaptive dose-escalation designs for simultaneously estimating the optimal and maximum safe dose based on safety and efficacy. , 2017, Pharmaceutical statistics.

[25]  Guosheng Yin,et al.  Clinical Trial Design: Bayesian and Frequentist Adaptive Methods , 2011 .

[26]  J O'Quigley,et al.  Dose‐Finding Designs for HIV Studies , 2001, Biometrics.

[27]  Guosheng Yin,et al.  Two-stage dose finding for cytostatic agents in phase I oncology trials. , 2013, Statistics in medicine.

[28]  Yuan Ji,et al.  Bayesian Dose‐Finding in Phase I/II Clinical Trials Using Toxicity and Efficacy Odds Ratios , 2006, Biometrics.

[29]  John Whitehead,et al.  Bayesian adaptive dose-escalation procedures for binary and continuous responses utilizing a gain function. , 2015, Pharmaceutical statistics.

[30]  Yuri M. Suhov,et al.  Basic inequalities for weighted entropies , 2015, ArXiv.

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

[32]  J. Aitchison On criteria for measures of compositional difference , 1992 .

[33]  Percy Ivy,et al.  Defining dose-limiting toxicity for phase 1 trials of molecularly targeted agents: results of a DLT-TARGETT international survey. , 2014, European journal of cancer.

[34]  Akihiro Hirakawa,et al.  An adaptive dose‐finding approach for correlated bivariate binary and continuous outcomes in phase I oncology trials , 2012, Statistics in medicine.

[35]  Thomas M Braun,et al.  The bivariate continual reassessment method. extending the CRM to phase I trials of two competing outcomes. , 2002, Controlled clinical trials.

[36]  Antje Hoering,et al.  Early phase trial design for assessing several dose levels for toxicity and efficacy for targeted agents , 2013, Clinical trials.

[37]  Nolan A Wages,et al.  Phase I/II adaptive design for drug combination oncology trials. , 2014, Statistics in medicine.

[38]  Thomas Jaki,et al.  An information theoretic approach for selecting arms in clinical trials , 2017, Journal of the Royal Statistical Society: Series B (Statistical Methodology).