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Continual reassessment method for partial ordering.

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TLDR
A new two-dimensional dose-finding method for multiple-agent trials that simplifies to the continual reassessment method (CRM), introduced by O'Quigley, Pepe, and Fisher (1990, Biometrics 46, 33-48), when the ordering is fully known, enables the assumption of a monotonic dose-toxicity curve to be relaxed.
Abstract
Summary. Much of the statistical methodology underlying the experimental design of phase 1 trials in oncology is 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 fundamental assumption of these methods is monotonicity of the dose–toxicity curve. This is a reasonable assumption for single-agent trials in which the administration of greater doses of the agent can be expected to produce dose-limiting toxicities in increasing proportions of patients. When studying multiple agents, the assumption may not hold because the ordering of the toxicity probabilities could possibly be unknown for several of the available drug combinations. At the same time, some of the orderings are known and so we describe the whole situation as that of a partial ordering. In this article, we propose a new two-dimensional dose-finding method for multiple-agent trials that simplifies to the continual reassessment method (CRM), introduced by O’Quigley, Pepe, and Fisher (1990, Biometrics 46, 33–48), when the ordering is fully known. This design enables us to relax the assumption of a monotonic dose–toxicity curve. We compare our approach and some simulation results to a CRM design in which the ordering is known as well as to other suggestions for partial orders.

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Citations
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Journal ArticleDOI

Adaptive Dose-Finding Studies: A Review of Model-Guided Phase I Clinical Trials

TL;DR: This review of completed phase I studies confirms the safety and generalizability of model-guided, adaptive dose-escalation designs, and it provides an approach for using, interpreting, and understanding such designs to guide dose escalation in phase I trials.
Journal ArticleDOI

Dose-finding design for multi-drug combinations:

TL;DR: A new dose-finding design which relaxes the monotonicity assumption and can serve as a link between single and multiple-agent dosefinding trials and can be considered a multivariate generalization of the CRM.
Journal ArticleDOI

Accuracy, Safety, and Reliability of Novel Phase I Trial Designs

TL;DR: The results show that theCRM outperforms EWOC and BLRM with higher accuracy of identifying the MTD, and the BOIN yields competitive performance comparable with the CRM but is simpler to implement and free of the issue of irrational dose assignment caused by model misspecification, thereby providing an attractive approach for designing phase I trials.
Journal ArticleDOI

A Bayesian dose finding design for oncology clinical trials of combinational biological agents

TL;DR: A novel dose finding algorithm is proposed to encourage sufficient exploration of untried dose combinations in the two‐dimensional space and has desirable operating characteristics in identifying the biologically optimal dose combination under various patterns of dose–toxicity and dose–efficacy relationships.
References
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Book

Order restricted statistical inference

TL;DR: In this paper, a set of multinomial parameters are derived about distributions subject to shape restrictions, and a conditional expectation given a sigma-lattice is given in a more general setting.
Journal ArticleDOI

Continual reassessment method: a practical design for phase 1 clinical trials in cancer.

TL;DR: A new approach to the design and analysis of Phase 1 clinical trials in cancer and a particularly simple model is looked at that enables the use of models whose only requirements are that locally they reasonably well approximate the true probability of toxic response.
Journal ArticleDOI

Design and analysis of phase I clinical trials.

TL;DR: In Monte Carlo simulations, two two-stage designs are found to provide reduced bias in maximum likelihood estimation of the MTD in less than ideal dose-response settings and several designs to be nearly as conservative as the standard design in terms of the proportion of patients entered at higher dose levels.
Journal ArticleDOI

Cancer phase I clinical trials: efficient dose escalation with overdose control

TL;DR: An adaptive dose escalation scheme for use in cancer phase I clinical trials that makes use of all the information available at the time of each dose assignment, and directly addresses the ethical need to control the probability of overdosing is described.
Journal ArticleDOI

Continual reassessment method: a likelihood approach.

TL;DR: This paper argues that such a framework is easily changed to a more classic one leaning upon likelihood theory, and suggests working with either a standard Up-and-Down scheme or standard continual reassessment method until toxicity is observed and then switching to the new scheme.
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