Already popular in the analysis of medical device trials, adaptive Bayesian designs are increasingly being used in drug development for a wide variety of diseases and conditions, from Alzheimer's disease and multiple sclerosis to obesity, diabetes, hepatitis C, and HIV. Written by leading pioneers of Bayesian clinical trial designs, Bayesian Adapti

This dissertation, "Bayesian Adaptive Methods for Phase I Clinical Trials" by Ruitao, Lin, 林瑞涛, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: The primary objective of phase I dose-finding trials is to determine the maximum tolerated dose (MTD), which is typically defined as the dose with the dose-limiting toxicity probability closest to the target toxicity probability. The American Society of Clinical Oncology (ASCO) recently published an update of the ASCO policy statement to call for new phase I trial designs to allow for more efficient escalation to the therapeutic dose levels in order to cope with the changing landscape in cancer research. In this thesis, innovative and robust designs for single- or multiple-agent phase I dose-finding trials are studied. To enhance robustness and efficiency, two nonparametric methods are proposed to locate the MTD in single-agent phase I clinical trials without imposing any parametric assumption on the underlying distribution of the toxicity curve. First, a uniformly most powerful Bayesian interval (UMPBI) design is developed for dose finding, where the optimal interval is determined by the rejection region of the uniformly most powerful Bayesian test. UMPBI is easy to implement and can be nicely interpreted. Compared with existing interval designs, the proposed UMPBI design exhibits a unique feature of convergence to the MTD. Next, a nonparametric overdose control (NOC) method is proposed by casting dose finding in a Bayesian model selection framework. Each dose assignment under NOC is determined such that the posterior probability of overdosing is controlled. In addition, NOC is incorporated with a fractional imputation method to deal with late-onset toxicity outcomes. Both of the UMPBI and NOC designs are flexible, as well as possessing sound theoretical support and desirable numerical performance. In the era of precision medicine, combination therapy is playing an increasingly important role in drug development. However, drug combinations often lead to a high-dimensional dose searching space compared to conventional single-agent dose finding, especially when three or more drugs are combined for treatment. Most of the current dose-finding designs aim to quantify the toxicity probability space using certain prespecified yet complicated models. Not only do these models characterize each individual drug's toxicity profile, but they also need to quantify their interaction effects, which often leads to multi-parameter models. In order to stabilize the current practice of dose finding in drug-combination trials with limited sample sizes, a random walk Bayesian optimal interval (RW-BOIN) design and a Bootstrap aggregating continual reassessment method (Bagging CRM) are proposed. RW-BOIN is built on the basis of the single-agent BOIN design, and it can be utilized to tackle high-dimensional dose-finding problems. A convergence theorem is established to ensure the validity of RW-BOIN. On the other hand, Bagging CRM implements a dimension reduction technique and some ensemble methods in machine learning, so that the toxicity probability space can be stably reduced to a one-dimensional searching line. Simulation studies show that both RW-BOIN and Bagging CRM have comparative and robust operating characteristics compared with existing approaches under various dose-toxicity scenarios. All of the proposed methods are exemplified with real phase I dose-finding trials. Subjects: Bayesian statistical decision theory Clinical trials - Statistical methods

A balanced treatment of the theories, methodologies, and design issues involved in clinical trials using statistical methods There has been enormous interest and development in Bayesian adaptive designs, especially for early phases of clinical trials. However, for phase III trials, frequentist methods still play a dominant role through controlling type I and type II errors in the hypothesis testing framework. From practical perspectives, Clinical Trial Design: Bayesian and Frequentist Adaptive Methods provides comprehensive coverage of both Bayesian and frequentist approaches to all phases of clinical trial design. Before underpinning various adaptive methods, the book establishes an overview of the fundamentals of clinical trials as well as a comparison of Bayesian and frequentist statistics. Recognizing that clinical trial design is one of the most important and useful skills in the pharmaceutical industry, this book provides detailed discussions on a variety of statistical designs, their properties, and operating characteristics for phase I, II, and III clinical trials as well as an introduction to phase IV trials. Many practical issues and challenges arising in clinical trials are addressed. Additional topics of coverage include: Risk and benefit analysis for toxicity and efficacy trade-offs Bayesian predictive probability trial monitoring Bayesian adaptive randomization Late onset toxicity and response Dose finding in drug combination trials Targeted therapy designs The author utilizes cutting-edge clinical trial designs and statistical methods that have been employed at the world's leading medical centers as well as in the pharmaceutical industry. The software used throughout the book is freely available on the book's related website, equipping readers with the necessary tools for designing clinical trials. Clinical Trial Design is an excellent book for courses on the topic at the graduate level. The book also serves as a valuable reference for statisticians and biostatisticians in the pharmaceutical industry as well as for researchers and practitioners who design, conduct, and monitor clinical trials in their everyday work.

Reliably optimizing a new treatment in humans is a critical first step in clinical evaluation since choosing a suboptimal dose or schedule may lead to failure in later trials. At the same time, if promising preclinical results do not translate into a real treatment advance, it is important to determine this quickly and terminate the clinical evaluation process to avoid wasting resources. Bayesian Designs for Phase I–II Clinical Trials describes how phase I–II designs can serve as a bridge or protective barrier between preclinical studies and large confirmatory clinical trials. It illustrates many of the severe drawbacks with conventional methods used for early-phase clinical trials and presents numerous Bayesian designs for human clinical trials of new experimental treatment regimes. Written by research leaders from the University of Texas MD Anderson Cancer Center, this book shows how Bayesian designs for early-phase clinical trials can explore, refine, and optimize new experimental treatments. It emphasizes the importance of basing decisions on both efficacy and toxicity.

Although adaptive design methods are flexible and useful in clinical research, little or no regulatory guidelines are available. One of the first books on the topic, Adaptive Design Methods in Clinical Trials presents the principles and methodologies in adaptive design and analysis that pertain to adaptations made to trial or statistical procedures

This book covers domains of modern clinical trial design: classical, group sequential, adaptive, and Bayesian methods applicable to and used in various phases of pharmaceutical development. Written for biostatisticians, pharmacometricians, clinical developers, and statistical programmers involved in the design, analysis, and interpretation of clinical trials, as well as students in graduate and postgraduate programs in statistics or biostatistics, it covers topics including: dose-response and dose-escalation designs; sequential methods to stop trials early for overwhelming efficacy, safety, or futility; Bayesian designs incorporating historical data; adaptive sample size re-estimation and randomization to allocate subjects to effective treatments; population enrichment designs. Methods are illustrated using clinical trials from diverse therapeutic areas, including dermatology, endocrinology, infectious disease, neurology, oncology and rheumatology. --