Date of Award

2018

Embargo Period

1-1-2021

Document Type

Dissertation - MUSC Only

Degree Name

Doctor of Philosophy (PhD)

Department

Public Health Sciences

College

College of Graduate Studies

First Advisor

Andrew Lawson

Second Advisor

Elizabeth Garrett-Mayer

Third Advisor

Elizabeth G. Hill

Fourth Advisor

Nolan Wages

Fifth Advisor

Carolyn Britten

Abstract

Conventional dose-finding cancer clinical trial methodology uses patient toxicity as the primary endpoint to identify the maximum tolerate dose (MTD). The MTD is defined as the highest dose from a set of doses that is considered safe base on pre-defined safety criteria, with respect to the expected patient toxicity at each dose level. Toxicities, commonly referred to as adverse events (AEs), are graded on a scale from 1 to 5, and are comprehensively defined by the Common Terminology Criteria for Adverse Events (CTCAE) guidelines published by the National Cancer Institute. Conventional trial methodology simplifies patient toxicity to a binary end-point, but recent attention in the literature has been given to the concept of using composite patient toxicity scores to better reflect a patients toxicity burden. In the current literature however, the methodology for establishing toxicity scores is based on a subjective process. This dissertation proposes a new method for establishing toxicity scores based on a statistical modeling framework, and applies toxicity scores in a novel way to drug-combination dose-finding trial methodology. In the first aim of this research, the Toxicity Score Elicitation Method (TSEM) is established as a statistical method for eliciting toxicity burden weights and toxicity scores to be applied to dose-finding cancer trials. The TSEM provides an accurate and efficient way to streamline the process of eliciting toxicity burden weights used to calculate toxicity scores. In the second aim this dissertation, the TSEM is extended in terms of its practical implementation. The TSEM is demonstrated through a point-and-click application that allows for intuitive use by clinical investigators, and the TSEM modeling framework is extended to accommodate the opinions of multiple clinical investigators when establishing toxicity burden weights. In the third aim of the dissertation, toxicity scores are applied to dose-finding drug-combination trial methodology, which demonstrate improved performance in terms of MTD selection in many scenarios compared to traditional methodology using a binary endpoint.

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