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Biomarker-Based Dose-Finding Designs for Single- or Multiple-Agent Phase I Trials

Xue, Yuan
Thesis/Dissertation; Online
Xue, Yuan
Conaway, Mark
Hu, Feifang
The primary goal of a Phase I clinical trial is to estimate the maximum tolerated dose (MTD). The MTD is defined as the highest dose which can be administered with a "tolerable" level of toxicity. The "tolerable" level of toxicity is based on the probability that a patient in the trial experiences a dose-limiting toxicity (DLT). This dissertation addresses two practical considerations: heterogeneous toxicity response and a partial ordering for the probability of toxicity for available treatments. First, the majority of methods for the Phase I trials are designed for a homogeneous toxicity response, resulting in a unique MTD for the broad patient population. However, patients may naturally differ in the way they react to a treatment. Clinical useful biomarkers which affect the probability of a DLT have been developed as more toxicity biomarker studies have been done. We propose a new design which chooses a distinct MTD for individual patient by use of toxicity biomarker information, thus contributing to a proper and better treatment for individual patient. Second, for multiple-agent trials we may be able to identify the order of the probability of toxicity for only a subset of the available treatments, which is a "partial order", in contrast to single-agent trial problems whose order of the probability of toxicity for all the treatments is fully known, which is a "simple order". We propose a biomarker-based design for which the ordering is not fully known. The operating characteristics of the proposed designs for simple order and partial order are investigated through extensive simulation studies. A discuss of the theoretical properties is provided. We also employ implementing model selection techniques, specifically BIC model selection method to improve the performance of the biomarker-based designs. We close with some conclusions drawn from the proposed biomarker-based designs, as well as some topics for further research.
University of Virginia, Department of Statistics, PHD, 2014
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