Böcker i Chapman & Hall/CRC Biostatistics Series-serien

This book concerns use of real world data (RWD) and real world evidence (RWE) to aid drug development across product cycle. RWD are healthcare data that are collected outside the constraints of conventual controlled randomized trials (CRTs); whereas RWE is the knowledge derived from aggregation and analysis of RWD.

This book shows how to model disease risk and quantify risk factors using areal and geostatistical data. It also shows how to create interactive maps of disease risk and risk factors, and describes how to build interactive dashboards and Shiny web applications that facilitate the communication of insights to collaborators and policy makers.

This book provides statisticians with an understanding of the critical challenges currently encountered in oncology trials. The book covers state-of-the-art approaches to the design and analysis of cancer clinical trials, such as adaptive designs, biomarker-based trials, and dynamic treatment regimes.

This book summarizes the author¿s experience in serving on many data monitoring committees and in heading up a contract research organization that provided statistical support to nearly seventy-five DMCs. It explains the difference in DMC operations between the pharmaceutical industry and National Institutes of Health -sponsored trials.

The book defines a unified framework for clinical trial optimization based on a comprehensive quantitative evaluation of relevant clinical scenarios (using the clinical scenario evaluation approach) and introduce best practices for simulationbased optimization.

Helping you become a skillful "simulator," this book provides broad coverage of the drug development process, from drug discovery to preclinical and clinical trial aspects to commercialization. It presents the theories and methods for carrying out computer simulations efficiently, covers both descriptive and pseudocode algorithms that provide the basis for implementing the simulation methods, and illustrates real-world problems through case studies. The author discusses many key topics, including game theory, adaptive design, molecular design, prescription drug marketing, biological pathway simulation, genetic programming, and pharmacokinetic modeling.

Clinical trials require strategic thinking and innovative methods since some traditional methods are not adequate for the 21st century.To solve these problems requires good methodological skills, but also an in-depth knowledge of the practical problems we are dealing with and a strategic vision of the pig picture.

The first book on the design and analysis of not only clinical trials but also how observational non-interventional data using clinical data is applied to economic evaluation and re-imbursement in the context of Cancer. This book is a non technical exposition of economic evaluation and no knowledge of advanced statistical methods is assumed.

Focuses on full analyses of real data sets based on the needs and requirements of end users. Fundamental theory is introduced with emphasis on the motivation by challenges coming from real problems with real data. Complete explanations of important concepts are provided, but the amount of mathematical derivation of formulation is minimized.

This book provides the first comprehensive account of the self-controlled case series (SCCS) method, a statistical method for investigating associations between outcome events and time-varying exposures.

This book focuses on clinical trial design and monitoring with time-to-event endpoints. Detail of subjects of the book are included in book contents. This book not only provides a comprehensive review of existing methods. The book also presents a general group sequential method for trial monitoring which has not been published in any book.

Medical Product Safety Evaluation: Biological Models and Statistical Methods presents cutting-edge biological models and statistical methods that are tailored to specific objectives and data types for safety analysis and benefit-risk assessment. Issues and challenges in the design and analysis of safety studies are covered.

Statistical Topics in Health Economics and Outcomes Research fulfils the need for a volume that presents a coherent and unified review of the major issues that arise in application, especially from a statistical perspective, by presenting an overview of the key analytical issues and best practice.

This practical book presents the main ideas for the analysis of repeated measures and associated designs from a Bayesian viewpoint. It describes many inferential methods for analyzing repeated measures in various scientific areas, especially biostatistics. The author bases all the computing and analysis on the WinBUGS package, which provides readers with a platform that efficiently uses prior information. The book includes the WinBUGS code needed to implement posterior analysis and offers the code for download online.

With complete coverage of issues in all phases of clinical trials, including design, data monitoring, analyses, interpretation, and reporting, this book provides an overview of fundamental concepts important to statisticians working with clinical trials. It examines issues related to product development and regulation, and then discusses the attributes that statisticians can acquire to maximize their contribution to the research. The co-authors draw on their experience in industry and academia to provide balanced, practical guidance on how statisticians can apply their knowledge to address real world clinical trial issues.

Encyclopedic in breadth, yet practical and concise, Medical Biostatistics, Fourth Edition focuses on the statistical aspects ofmedicine with a medical perspective, showing the utility of biostatistics as a tool to manage many medical uncertainties. This edition includes more topics in order to fill gaps in the previous edition.

This book provides a broad perspective of new quantitative methods in HIV/AIDS research, contributed by those immersed in HIV research. It is the editors' hope that the work will inspire more statisticians, mathematicians and computer scientists to collaborate and contribute to the interdisciplinary challenges of addressing the AIDS pandemic.

Advancing the development, validation, and use of PRO measures, this book helps readers develop and enrich their understanding of PRO methodology, particularly from a quantitative perspective. Designed for biopharmaceutical researchers and others in the health sciences community, it provides an up-to-date volume on conceptual and analytical issues of PRO measures. The authors include many real-life applications and implement a wealth of simulated datasets to illustrate concepts. Most chapters contain SAS code and output that demonstrate the methodology.

Get Up to Speed on Many Types of Adaptive Designs Since the publication of the first edition, there have been remarkable advances in the methodology and application of adaptive trials. Incorporating many of these new developments, Adaptive Design Theory and Implementation Using SAS and R, Second Edition offers a detailed framework to understand the use of various adaptive design methods in clinical trials. New to the Second Edition Twelve new chapters covering blinded and semi-blinded sample size reestimation design, pick-the-winners design, biomarker-informed adaptive design, Bayesian designs, adaptive multiregional trial design, SAS and R for group sequential design, and much more More analytical methods for K-stage adaptive designs, multiple-endpoint adaptive design, survival modeling, and adaptive treatment switching New material on sequential parallel designs with rerandomization and the skeleton approach in adaptive dose-escalation trials Twenty new SAS macros and R functions Enhanced end-of-chapter problems that give readers hands-on practice addressing issues encountered in designing real-life adaptive trials Covering even more adaptive designs, this book provides biostatisticians, clinical scientists, and regulatory reviewers with up-to-date details on this innovative area in pharmaceutical research and development. Practitioners will be able to improve the efficiency of their trial design, thereby reducing the time and cost of drug development.

This book shows how to apply biclustering methods to find local patterns in a big data matrix. It presents an overview of data analysis using biclustering methods from a practical point of view. Real case studies in drug discovery, genetics, marketing research, biology, toxicity, and sports illustrate the use of several biclustering methods. All the methods are accompanied with R examples that show how to conduct the analyses. The examples, software, and other materials are available on a supplementary website.

The main goal of this book is to define a unified framework for clinical trial optimization based on a comprehensive quantitative evaluation of relevant clinical scenarios (using the clinical scenario evaluation approach) and introduce best practices for simulationbased optimization. The book will be aimed at a broad audience and will emphasize a hands-on approach with a detailed discussion of practical issues arising in clinical trial optimization and R software implementation (relevant statistical methodology will be moved to the appendix).