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.
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.
|Conaway, Mark R; Petroni, Gina R (2018) The Impact of Early-Phase Trial Design in the Drug Development Process. Clin Cancer Res :|
|Wages, Nolan A; Petroni, Gina R (2018) A web tool for designing and conducting phase I trials using the continual reassessment method. BMC Cancer 18:133|
|Wages, Nolan A; Conaway, Mark R (2018) Revisiting isotonic phase I design in the era of model-assisted dose-finding. Clin Trials 15:524-529|
|Iasonos, Alexia; O'Quigley, John (2017) Phase I Designs that Allow for Uncertainty in the Attribution of Adverse Events. J R Stat Soc Ser C Appl Stat 66:1015-1030|
|Conaway, Mark (2017) Isotonic designs for phase I trials in partially ordered groups. Clin Trials 14:491-498|
|Conaway, Mark R; Wages, Nolan A (2017) Designs for phase I trials in ordered groups. Stat Med 36:254-265|
|Wages, Nolan A; Varhegyi, Nikole (2017) A web application for evaluating Phase I methods using a non-parametric optimal benchmark. Clin Trials 14:553-557|
|Wages, N A; Slingluff Jr, C L; Petroni, G R (2017) Statistical controversies in clinical research: early-phase adaptive design for combination immunotherapies. Ann Oncol 28:696-701|
|Petroni, Gina R; Wages, Nolan A; Paux, Gautier et al. (2017) Implementation of adaptive methods in early-phase clinical trials. Stat Med 36:215-224|
|Iasonos, Alexia; O'Quigley, John (2017) Sequential monitoring of Phase I dose expansion cohorts. Stat Med 36:204-214|
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