This book provides a comprehensive introduction to statistical methods for designing early phase dose-finding clinical trials. It will serve as a textbook or handbook for graduate students and practitioners in biostatistics and clinical investigators who are involved in designing, conducting, monitoring, and analyzing dose-finding trials. The book will also provide an overview of advanced topics and discussions in this field for the benefit of researchers in biostatistics and statistical science. Beginning with backgrounds and fundamental notions on dose finding in early phase clinical trials, the book then provides traditional and recent dose-finding designs of phase I trials for, e.g., cytotoxic agents in oncology, to evaluate toxicity outcome. Included are rule-based and model-based designs, such as 3 + 3 designs, accelerated titration designs, toxicity probability interval designs, continual reassessment method and related designs, and escalation overdose control designs. This book also covers more complex and updated dose-finding designs of phase I-II and I/II trials for cytotoxic agents, and cytostatic agents, focusing on both toxicity and efficacy outcomes, such as designs with covariates and drug combinations, maximum tolerated dose-schedule finding designs, and so on.
This book deals with advanced methods for adaptive phase I dose-finding clinical trials for combination of two agents and molecularly targeted agents (MTAs) in oncology. It provides not only methodological aspects of the dose-finding methods, but also software implementations and practical considerations in applying these complex methods to real cancer clinical trials. Thus, the book aims to furnish researchers in biostatistics and statistical science with a good summary of recent developments of adaptive dose-finding methods as well as providing practitioners in biostatistics and clinical investigators with advanced materials for designing, conducting, monitoring, and analyzing adaptive dose-finding trials. The topics in the book are mainly related to cancer clinical trials, but many of those topics are potentially applicable or can be extended to trials for other diseases. The focus is mainly on model-based dose-finding methods for two kinds of phase I trials. One is clinical trials with combinations of two agents. Development of dose-finding methods for two-agent combination trials requires reasonable models that can adequately capture joint toxicity probabilities for two agents, taking into consideration possible interactions of the two agents on toxicity probability such as synergistic or antagonistic effects. Another is clinical trials for evaluating both efficacy and toxicity outcomes in single- and two-agent combination trials. These methods are often applied to the phase I trials including MTAs because the toxicity and efficacy for a MTA does not monotonically increase with dose, but the efficacy often increases initially with the dose and then plateaus. Successful software implementations for several dose-finding methods are introduced in the book, and their operating characteristics in practice are discussed. Recent advance of the adaptive dose-finding methods in drug developments are also provided.
This book introduces readers to advanced statistical methods for analyzing survival data involving correlated endpoints. In particular, it describes statistical methods for applying Cox regression to two correlated endpoints by accounting for dependence between the endpoints with the aid of copulas. The practical advantages of employing copula-based models in medical research are explained on the basis of case studies. In addition, the book focuses on clustered survival data, especially data arising from meta-analysis and multicenter analysis. Consequently, the statistical approaches presented here employ a frailty term for heterogeneity modeling. This brings the joint frailty-copula model, which incorporates a frailty term and a copula, into a statistical model. The book also discusses advanced techniques for dealing with high-dimensional gene expressions and developing personalized dynamic prediction tools under the joint frailty-copula model. To help readers apply the statistical methods to real-world data, the book provides case studies using the authors’ original R software package (freely available in CRAN). The emphasis is on clinical survival data, involving time-to-tumor progression and overall survival, collected on cancer patients. Hence, the book offers an essential reference guide for medical statisticians and provides researchers with advanced, innovative statistical tools. The book also provides a concise introduction to basic multivariate survival models.
This title contains the most up-to-date and comprehensive information on the development of the Charge-Coupled Device (CCD), which makes possible the widespread use of consumer camcorders and broadcasting color cameras. It is comprehensive enough to be of great value to researchers, industrialists and post-graduate students in image technology.
Soft matter is a concept which covers polymers, liquid crystals, colloids, amphiphilic molecules, glasses, granular and biological materials. One of the fundamental characteristic features of soft matter is that it exhibits various mesoscopic structures originating from a large number of internal degrees of freedom of each molecule. Due to such intermediate structures, soft matter can easily be brought into non-equilibrium states and cause non-linear responses by imposing external fields such as an electric field, a mechanical stress or a shear flow. Volume 4 of the series in Soft Condensed Matter focuses on the non-linear and non-equilibrium properties of soft matter. It contains a collection of review articles on the current topics of non-equilibrium soft matter physics written by leading experts in the field. The topics dealt with in this volume includes rheology of polymers and liquid crystals, dynamical properties of Langmuir monolayers at the air/water interface, hydrodynamics of membranes and twisted filaments as well as dynamics of deformable self-propelled particles and migration of biological cells. This book serves both as an introduction to students as well as a useful reference to researchers.
This book provides a comprehensive introduction to statistical methods for designing early phase dose-finding clinical trials. It will serve as a textbook or handbook for graduate students and practitioners in biostatistics and clinical investigators who are involved in designing, conducting, monitoring, and analyzing dose-finding trials. The book will also provide an overview of advanced topics and discussions in this field for the benefit of researchers in biostatistics and statistical science. Beginning with backgrounds and fundamental notions on dose finding in early phase clinical trials, the book then provides traditional and recent dose-finding designs of phase I trials for, e.g., cytotoxic agents in oncology, to evaluate toxicity outcome. Included are rule-based and model-based designs, such as 3 + 3 designs, accelerated titration designs, toxicity probability interval designs, continual reassessment method and related designs, and escalation overdose control designs. This book also covers more complex and updated dose-finding designs of phase I-II and I/II trials for cytotoxic agents, and cytostatic agents, focusing on both toxicity and efficacy outcomes, such as designs with covariates and drug combinations, maximum tolerated dose-schedule finding designs, and so on.
This book provides an overview of the statistical methods used in genome-wide screening of relevant genomic features or genes. Gene screening can facilitate deeper understanding of disease biology at the molecular level, possibly leading to discovery of new molecular targets for developing new treatments and developing diagnostic tests to predict patients’ prognosis or response to treatment. The most common approach to such gene screening studies is to apply multiple univariate analysis based on separate statistical tests for individual genes to test the null hypothesis of no association with clinical variables. This book first provides an overview of the state of the art of such multiple testing methodologies for gene screening, including frequentist multiple tests, empirical Bayes, and full-Bayes model-based methods for controlling the family-wise error rate or false discovery rate. Optimal discovery procedures and model-based variants are also discussed. Although great endeavor has been directed toward developing multiple testing methods, there are other, more relevant and effective analyses that should be given much attention in gene screening, including gene ranking, estimation of effect sizes, and classification accuracy based on selected genes. The core contents of this book provide a framework for integrated gene screening analysis based on hierarchical mixture modeling and empirical Bayes. Within this framework effective tools for multiple testing, ranking, estimation of effect size, and classification accuracy are derived. Methods for sample size determination for gene screening studies are also provided. With this content, the book is certain to expand the existing framework of statistical analysis based on multiple testing for gene screening to one based on estimation and selection.
This book deals with advanced methods for adaptive phase I dose-finding clinical trials for combination of two agents and molecularly targeted agents (MTAs) in oncology. It provides not only methodological aspects of the dose-finding methods, but also software implementations and practical considerations in applying these complex methods to real cancer clinical trials. Thus, the book aims to furnish researchers in biostatistics and statistical science with a good summary of recent developments of adaptive dose-finding methods as well as providing practitioners in biostatistics and clinical investigators with advanced materials for designing, conducting, monitoring, and analyzing adaptive dose-finding trials. The topics in the book are mainly related to cancer clinical trials, but many of those topics are potentially applicable or can be extended to trials for other diseases. The focus is mainly on model-based dose-finding methods for two kinds of phase I trials. One is clinical trials with combinations of two agents. Development of dose-finding methods for two-agent combination trials requires reasonable models that can adequately capture joint toxicity probabilities for two agents, taking into consideration possible interactions of the two agents on toxicity probability such as synergistic or antagonistic effects. Another is clinical trials for evaluating both efficacy and toxicity outcomes in single- and two-agent combination trials. These methods are often applied to the phase I trials including MTAs because the toxicity and efficacy for a MTA does not monotonically increase with dose, but the efficacy often increases initially with the dose and then plateaus. Successful software implementations for several dose-finding methods are introduced in the book, and their operating characteristics in practice are discussed. Recent advance of the adaptive dose-finding methods in drug developments are also provided.
This book introduces readers to advanced statistical methods for analyzing survival data involving correlated endpoints. In particular, it describes statistical methods for applying Cox regression to two correlated endpoints by accounting for dependence between the endpoints with the aid of copulas. The practical advantages of employing copula-based models in medical research are explained on the basis of case studies. In addition, the book focuses on clustered survival data, especially data arising from meta-analysis and multicenter analysis. Consequently, the statistical approaches presented here employ a frailty term for heterogeneity modeling. This brings the joint frailty-copula model, which incorporates a frailty term and a copula, into a statistical model. The book also discusses advanced techniques for dealing with high-dimensional gene expressions and developing personalized dynamic prediction tools under the joint frailty-copula model. To help readers apply the statistical methods to real-world data, the book provides case studies using the authors’ original R software package (freely available in CRAN). The emphasis is on clinical survival data, involving time-to-tumor progression and overall survival, collected on cancer patients. Hence, the book offers an essential reference guide for medical statisticians and provides researchers with advanced, innovative statistical tools. The book also provides a concise introduction to basic multivariate survival models.
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