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Activity Number:
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552
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Type:
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Topic Contributed
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Date/Time:
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Thursday, August 6, 2009 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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| Abstract - #303635 |
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Title:
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Dose-Schedule Finding in Phase I/II Clinical Trials Using a Bayesian Isotonic Transformation
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Author(s):
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Benjamin N. Bekele*+ and Yisheng Li and Yuan Ji
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Companies:
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The University of Texas M.D. Anderson Cancer Center and The University of Texas M.D. Anderson Cancer Center and The University of Texas M.D. Anderson Cancer Center
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Address:
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1400 Pressler St., Houston, TX, 77030,
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Keywords:
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Bayesian Adaptive Design ; Matrix Order ; Partial Order ; Bayesian Isotonic Regression ; Dose-finding ; Dose-Schedule-Finding
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Abstract:
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A dose-schedule-finding (DSF) trial is a new type of oncology trial that aims to find a safe and effective combination of dose and schedule. We show that a major difference between traditional dose-finding and DSF trials is that while the toxicity probabilities follow a simple non-decreasing order in dose-finding trials, DSF trials may adhere to a matrix order. We propose a Bayesian model that jointly models the unordered probabilities of toxicity and efficacy, and apply a Bayesian isotonic transformation to the posterior samples of the toxicity probabilities, so that the transformed posterior samples adhere to the matrix order constraint. Based on the joint posterior of the order-constrained toxicity and the unordered efficacy probabilities, we develop a criteria-based DSF algorithm that sequentially assigns patients to dose-schedule combinations.
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