Books in the Chapman & Hall/CRC Biostatistics Series series

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.

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.

This resource provides a thorough presentation of the design, monitoring, analysis, and interpretation of clinical trials in which time-to-event is of critical interest. Incorporating the collaborations of truly world-class statisticians, it discusses the design and monitoring of Phase II and III clinical trials with time-to-event endpoints. The book presents parametric, semiparametric, categorical, and Bayesian inferential and descriptive methods for analyzing time-to-event endpoints and covers numerous clinical trial applications, including analgesic, antibiotic, antiviral, cancer and cardiovascular prevention, optimal assignment of treatments, adverse events, and carcinogenicity.

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.

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).

The aim of this book is to equip biostatisticians and other quantitative scientists with the necessary skills, knowledge, and habits to collaborate effectively with clinicians in the healthcare field. The book provides valuable insight on where to look for information and material on sample size and statistical techniques commonly used in clinical research, and on how best to communicate with clinicians. It also covers the best practices to adopt in terms of project, time, and data management; relationship with collaborators; etc.

This book is the first to focus on Bayesian phase I¿II clinical trials. It describes many problems with the conventional phase I¿phase II paradigm and covers a large number of modern Bayesian phase I¿II clinical trial designs.

Statistical Methods for Healthcare Performance Monitoring covers measuring quality, types of data, risk adjustment, defining good and bad performance, statistical monitoring, presenting the results to different audiences and evaluating the monitoring system itself. Using examples from around the world, it brings all the issues and perspectives together in a largely non-technical way for clinicians, managers and methodologists.

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.

This guide explains how to apply health economic evaluation techniques to both clinical trial and non-clinical trial data. Through a simple, applied approach using examples and SAS software, the book helps statisticians and researchers in health economics assess cost-effectiveness. It covers trial design, case report form design, quality of life measures, sample sizes, submissions to regulatory authorities for reimbursement, Markov models, cohort models, and decision trees. Examples and case studies are provided at the end of each chapter.

This book provides statisticians with an understanding of the critical challenges currently encountered in oncology trials. Top researchers in academia, government, and the industry present many examples drawn from real 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. It also explains the importance of the selection of endpoints, use of historical data, multiplicity, analysis of safety data, discovery and validation of predictive signatures, dose-finding, sample size, and non-inferiority designs.

This book aids in identifying when to apply adaptive randomization designs in practice. Featuring contributions from statisticians, clinical trialists, and subject-matter experts in academia and industry, the text discusses restricted, covariate-adaptive, response-adaptive, and covariate-adjusted response-adaptive randomization designs, as well as randomized designs with treatment selection. It describes a successful implementation of a single pivotal phase II/III adaptive trial in infants with proliferating hemangioma, addresses practical aspects of phase II dose-ranging studies, and examines statistical monitoring and interim analysis issues in response-adaptive randomized clinical trials.

Written by a team of experienced leaders, this book brings the most advanced knowledge and statistical methods of drug safety to the statistical, clinical, and safety community. It explains design, monitoring, analysis, and reporting issues for both clinical trials and observational studies in biopharmaceutical product development. The book addresses key challenges across regulatory agencies, industry, and academia. It discusses quantitative approaches to safety evaluation and risk management in drug development, covering Bayesian methods, effective safety graphics, and risk-benefit evaluation.

This book offers statistical guidance on conducting clinical trials for predictive medicine. It covers statistical topics relevant to the main clinical research phases for developing molecular diagnostics and therapeutics. The book explains how to identify molecular biomarkers using DNA microarrays, validate the developed biomarkers, and confirm their clinical utility in randomized clinical trials.

This book provides wide-ranging coverage of parametric modeling in linear and nonlinear mixed effects models. It presents a rigorous approach for describing, implementing, and using mixed effects models. With these models, readers can perform parameter estimation and modeling across a whole population of individuals at the same time. The book takes readers through the whole modeling process, from defining/creating a parametric model to performing tasks on the model using various mathematical methods. Numerous examples illustrate how to implement the models using the Monolix software.

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.