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.
From Sung times, and throughout the Ming period, one of the dominant philosophies of China had been a dualistic rationalism thought to be firmly grounded on the classics. Tai Chen (1723-1777) was a scholar and philosopher during the Ch'ing period- a time when China produced few philosophic thinkers. He was the greatest of these, and his views are embodied chiefly in Yuan Shan and in Meng Tzu txu-yi shu-cheng. In place of the prevailing Sung dualism, Tai Chen propounded a rationalistic monism seldom before insinuated in a Chinese philosophy. He declines to accept current dogmas and preferred to seek his own truths. His commentaries opposed the time-honored interpretations of Chu Hsi, and he discredited them on purely philosophical grounds. But with few disciples to carry on his teachings, he was virtually forgotten or ignored in China for more than a hundred years after his death. It was not until early in the present century- with China under the pressures of Western aggression and internal disorders-that Tai Chen's nearness to Western thought was rediscovered and his important role in the history of philosophy recognized. Curiously, this first of China's Western-oriented philosophers even today remains little known in the West and his major writings largely untranslated.
Bayesian adaptive designs provide a critical approach to improve the efficiency and success of drug development that has been embraced by the US Food and Drug Administration (FDA). This is particularly important for early phase trials as they form the basis for the development and success of subsequent phase II and III trials. The objective of this book is to describe the state-of-the-art model-assisted designs to facilitate and accelerate the use of novel adaptive designs for early phase clinical trials. Model-assisted designs possess avant-garde features where superiority meets simplicity. Model-assisted designs enjoy exceptional performance comparable to more complicated model-based adaptive designs, yet their decision rules often can be pre-tabulated and included in the protocol—making implementation as simple as conventional algorithm-based designs. An example is the Bayesian optimal interval (BOIN) design, the first dose-finding design to receive the fit-for-purpose designation from the FDA. This designation underscores the regulatory agency's support of the use of the novel adaptive design to improve drug development. Features Represents the first book to provide comprehensive coverage of model-assisted designs for various types of dose-finding and optimization clinical trials Describes the up-to-date theory and practice for model-assisted designs Presents many practical challenges, issues, and solutions arising from early-phase clinical trials Illustrates with many real trial applications Offers numerous tips and guidance on designing dose finding and optimization trials Provides step-by-step illustrations of using software to design trials Develops a companion website (www.trialdesign.org) to provide freely available, easy-to-use software to assist learning and implementing model-assisted designs Written by internationally recognized research leaders who pioneered model-assisted designs from the University of Texas MD Anderson Cancer Center, this book shows how model-assisted designs can greatly improve the efficiency and simplify the design, conduct, and optimization of early-phase dose-finding trials. It should therefore be a very useful practical reference for biostatisticians, clinicians working in clinical trials, and drug regulatory professionals, as well as graduate students of biostatistics. Novel model-assisted designs showcase the new KISS principle: Keep it simple and smart!
This book provides a comprehensive review of novel adaptive trial designs for targeted therapies and immunotherapies. This book covers a wide range of novel statistical designs for various clinical settings, including early phase dose-escalation study, proof-of-concept trials, and confirmatory studies with registrational. The book includes real-life examples and software to facilitate practitioners to learn and use the designs in practice.
The Tragedy of 228: Historical Truth and Transitional Justice in Taiwan is published by Memorial Foundation of 228. The book mainly explores eight different aspects of the 228 Incident, as shown in each of its chapter title: “What Caused the Incident: A Comparison of Government and Citizen Accounts from 1947,” “International Perspectives on the Legal Status of Taiwan during and after the Incident,” “Military Deployment and Suppression during the Incident,” “Taiwan's News Media under the Impact of the Incident,” “The Roles of Local Government Heads during the Incident,” “The Roles of Intelligence Agencies during the Incident,” “Historical Explorations of the Campaign to Redress Injustices of the Incident (1987-1997),” and “Presidential Attitudes towards the Movement for Transitional Justice (1988-2019).” Through the publication of the book, the Memorial Foundation of 228 attempts to set a new milestone in the study of 228 Incident by pursuing the transitional justice in international academic communities, as well as introducing and promoting Modern Taiwanese History to the world by featuring its unique social culture, geographical surroundings, and political transformations.
Helping vulnerable children develop their full potential is an attractive idea with broad common-sense appeal. However, child well-being is a broad concept, and the legislative mandate for addressing well-being in the context of the current child welfare system is not particularly clear. This volume asserts that finding a place for well-being on the list of outcomes established to manage the child welfare system is not as easy as it first appears. The overall thrust of this argument is that policy should be evidence-based, and the available evidence is a primary focus of the book. Because policymakers have to make decisions that allocate resources, a basic understanding of incidence in the public health tradition is important, as is evidence that speaks to the question of what works clinically. The rest of the book addresses the evidence. Chapter 2 integrates bio-ecological and public health perspectives to give the evidence base coherence. Chapters 3 and 4 combine evidence from the National Child Abuse and Neglect Data System, the Multistate Foster Care Data Archive, and the National Survey of Child and Adolescent Well-Being to offer an unprecedented profile of children as they enter the child welfare system. Chapters 5 and 6 address the broad question of what works. A concluding chapter focuses on policy and future directions, suggesting that children starting out, children starting school, and children starting adolescence are high-risk populations for which explicit strategies have to be formed. This timely volume offers useful insights into the child welfare system and will be of particular interest to policymakers, academics with an interest in Child Welfare Policy, Social Work educators, and Child Advocates.
This book is the last work of the author’s trilogy on Chinese rural politics. In the background of prominent social conflicts since the 1990s during China’s social transformation, the author conducted in-depth comparative analyses of several conflicts to understand the changes in goals, driving forces, and operating systems in the Chinese rural group contentions. His analyses also focused the changes in techniques and strategies of the governments’ stability maintenance, as well as the complicated social and political consequences brought by these changes. This book applies a very unique perspective – “vigor” in the Chinese culture – to understand contemporary rural contentious politics, in an attempt to overcome the problem brought by sense and sensibility and the confrontation between power and morality in the current contentious politics studies. And such a perspective successfully avoids the opposition between the transplanting school and rural school, which pushes forward the frontier of research on contentious political theories and rural societies.
This book provides a comprehensive review and in-depth discussion of the state-of-the-art research literature and propose energy-efficient computation offloading and resources management for mobile edge computing (MEC), covering task offloading, channel allocation, frequency scaling and resource scheduling. Since the task arrival process and channel conditions are stochastic and dynamic, the authors first propose an energy efficient dynamic computing offloading scheme to minimize energy consumption and guarantee end devices’ delay performance. To further improve energy efficiency combined with tail energy, the authors present a computation offloading and frequency scaling scheme to jointly deal with the stochastic task allocation and CPU-cycle frequency scaling for minimal energy consumption while guaranteeing the system stability. They also investigate delay-aware and energy-efficient computation offloading in a dynamic MEC system with multiple edge servers, and introduce an end-to-end deep reinforcement learning (DRL) approach to select the best edge server for offloading and allocate the optimal computational resource such that the expected long-term utility is maximized. Finally, the authors study the multi-task computation offloading in multi-access MEC via non-orthogonal multiple access (NOMA) and accounting for the time-varying channel conditions. An online algorithm based on DRL is proposed to efficiently learn the near-optimal offloading solutions. Researchers working in mobile edge computing, task offloading and resource management, as well as advanced level students in electrical and computer engineering, telecommunications, computer science or other related disciplines will find this book useful as a reference. Professionals working within these related fields will also benefit from this book.
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