Books in the Chapman & Hall/CRC Biostatistics Series series

This comprehensive reference covers all aspects of drug combination research, from designing in vitro drug combination studies to analyzing clinical trial data. Featuring contributions from researchers in industry, academia, and regulatory agencies, this balanced text provides researchers with a solid understanding of the available statis

Emphasizes the importance of statistical thinking in clinical research and presents the methodology as a key component of clinical research. From ethical issues and sample size considerations to adaptive design procedures and statistical analysis, the book first covers the methodology that spans various clinical trials.

Helping you become a creative, logical thinker and skillful 'simulator,' this book provides coverage of the entire drug development process, from drug discovery to preclinical and clinical trial aspects to commercialization. It presents the theories and methods needed to carry out computer simulations efficiently.

Explains how to solve important problems in multiple testing encountered in drug discovery, pre-clinical, and clinical trial applications. This book presents relevant statistical methodology; illustrates the methodology using real-life examples from drug discovery experiments; and provides software code for solving the problems.

Brings together a body of research and discusses the issues involved in the design of a non-inferiority trial. This book uses examples from real clinical trials, and discusses general and regulatory issues and illustrates how they affect analysis. It also provides mathematical approaches along with their mathematical properties.

Provides a presentation of the design, monitoring, analysis, and interpretation of clinical trials in which time-to-event is of critical interest. This book discusses the design and monitoring of Phase II and III clinical trials with time-to-event endpoints.

Illustrating how stability studies play an important role in drug safety and quality assurance, this book introduces the basic concepts of stability testing. It focuses on short-term stability studies, and reviews several methods for estimating drug expiration dating periods.

This book will present a unified and up-to date introduction to ROC methodologies, covering both diagnosis (classification) and prediction. The book will emphasize the practical implementation of these methods using standard statistical software such as R and STATA.

This book presents the advanced statistical methods for mapping pharmacogenetic control by integrating pharmacokinetic and pharmacodynamic principles of drug-body interactions. This book is suitable for graduate students and researchers in the field of biology, medicine, bioinformatics and drug design and delivery.

Presenting an introductory perspective to modern Bayesian procedures, this work explores Bayesian principles and illustrates their application to healthcare research. It focuses on the history and mathematical foundation of Bayesian procedures, before discussing their implementation in healthcare research from first principles.

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.

Focusing on visualization and computational approaches with an emphasis on the importance of simulation, this work introduces modern and classical biostatistical methods and compares their usefulness. It covers topics in biostatistical science, including simple linear regression, multivariate regression, repeated measure and sample size.

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 volume covers the main areas of quantitative methodology for the design and analysis of CER studies. The volume has four major sections¿causal inference; clinical trials; research synthesis; and specialized topics. The audience includes CER methodologists, quantitative-trained researchers interested in CER, and graduate students in statistics, epidemiology, and health services and outcomes research. The book assumes a masters-level course in regression analysis and familiarity with clinical research.

Offers a presentation of various activities and results in bioavailability and bioequivalence on regulatory requirements, scientific and practical issues, and statistical methodology. This book covers statistical problems that may occur in the various stages of design and data analysis.

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).

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.

Artificial Intelligence for Drug Development, Precision Medicine, and Healthcare covers exciting developments at the intersection of computer science and statistics.

Cluster Randomised Trials, Second Edition explores the advantages of cluster randomisation, with special attention given to evaluating the effects of interventions against infectious diseases. Avoiding unnecessary mathematical detail, it covers basic concepts underlying the use of cluster randomisation.

This remarkable text raises the analysis of data in health sciences and policy to new heights of refinement and applicability by introducing cutting-edge meta-analysis strategies while reviewing more commonly used techniques.

Bayesian methods have emerged as the driving force for methodological development in drug development. This edited book provides broad coverage of Bayesian methods in pharmaceutical research. The book includes contributions from some of the leading researchers in the field, and has been edited to ensure consistency in level and style.

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.