The majority of methods for the design of Phase I trials for in oncology are intended for studies involving a single cytotoxic agent. The goal of these studies is to estimate the 'maximally tolerated dose', the highest dose that can be administered with an acceptable level of toxicity. A key assumption of these methods is the monotonicity of the dose- response curve. In this case, the dose-response curve is said to follow a 'simple order' because the ordering of the probabilities of a 'dose-limiting toxicity'(DLT) for any pair of doses is known;administration of greater doses of the agent can be expected to produce DLT's in increasing proportions of patients. It is becoming increasingly common for combinations of agents to be tested in phase I trials. In these studies, the probabilities of a DLT associated with the dose combinations often follow a 'partial order'in that there are pairs of dose combinations for which the ordering of the probabilities is not known. This proposal uses Bayesian methods, combining features of the continual reassessment method and order restricted inference to develop designs for phase I trials in which the probabilities follow a partial order. In addition, we will adapt our methods for cycle-specific toxicities. Finally, we will develop internet-accessible software to assist users in designing and carrying out partially ordered phase I trials. Even though our emphasis is on phase I trials of combinations, the methods we develop can shed light on other issues in phase I trial design, including the study of ordered groups and trials of cancer vaccines.

Public Health Relevance

Dose-finding trials of combinations of agents are becoming increasingly common in cancer research. While there are many proposed methods for single agent trials, there are relatively few options for designing phase I trials of combinations. As in single agent trials, it is crucial to find a combination of doses that can be administered with an acceptable level of toxicity in order that these new therapies can be tested for efficacy. Without adequate statistical methods potentially effective combinations may be discarded as too toxic or get tested in subsequent studies at sub-optimal dose combinations. The overall goal of this proposal is to develop designs for phase I trials of combinations of agents.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project (R01)
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Clinical Oncology Study Section (CONC)
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Witherspoon, Kim
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University of Virginia
Public Health & Prev Medicine
Schools of Medicine
United States
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Iasonos, Alexia; O'Quigley, John (2014) Adaptive dose-finding studies: a review of model-guided phase I clinical trials. J Clin Oncol 32:2505-11
Wages, Nolan A; Conaway, Mark R (2014) Phase I/II adaptive design for drug combination oncology trials. Stat Med 33:1990-2003
O'Quigley, John; Iasonos, Alexia (2014) Bridging Solutions in Dose Finding Problems. Stat Biopharm Res 6:185-197
Wages, Nolan A; Conaway, Mark R; O'Quigley, John (2014) Comments on 'A dose-finding approach based on shrunken predictive probability for combinations of two agents in phase I trials' by Akihiro Hirakawa, Chikuma Hamada, and Shigeyuki Matsui. Stat Med 33:2156-8
Wages, Nolan A; O'Quigley, John; Conaway, Mark R (2014) Phase I design for completely or partially ordered treatment schedules. Stat Med 33:569-79
Wages, Nolan A; Conaway, Mark R (2013) Specifications of a continual reassessment method design for phase I trials of combined drugs. Pharm Stat 12:217-24
Wages, Nolan A; Conaway, Mark R; O'Quigley, John (2013) Using the time-to-event continual reassessment method in the presence of partial orders. Stat Med 32:131-41
Wages, Nolan A; Varhegyi, Nikole (2013) pocrm: an R-package for phase I trials of combinations of agents. Comput Methods Programs Biomed 112:211-8
Wages, Nolan A; Conaway, Mark R; O'Quigley, John (2011) Dose-finding design for multi-drug combinations. Clin Trials 8:380-9
Iasonos, Alexia; Zohar, Sarah; O'Quigley, John (2011) Incorporating lower grade toxicity information into dose finding designs. Clin Trials 8:370-9

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