Böcker i Chapman & Hall/CRC Biostatistics Series-serien

Biosimilars have the potential to change the way we think about, identify, and manage health problems. They are already impacting both clinical research and patient care, and this impact will only grow as our understanding and technologies improve. Written by a team of experienced specialists in clinical development, this book discusses various potential drug development strategies, the design and analysis of pharmacokinetics (PK) studies, and the design and analysis of efficacy studies.

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.

The premise of Quality by Design (QbD) is that the quality of the pharmaceutical product should be based upon a thorough understanding of both the product and the manufacturing process. This state-of-the-art book provides a single source of information on emerging statistical approaches to QbD and risk-based pharmaceutical development. A comprehensive resource, it combines in-depth explanations of advanced statistical methods with real-life case studies that illustrate practical applications of these methods in QbD implementation.

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.

Focusing on the practical clinical and statistical issues that arise in pharmaceutical industry trials, this book summarizes the author's experience in serving on many data monitoring committees (DMCs) 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 (NIH)-sponsored trials.

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 a compendium of statistical approaches for decision making, ranging from graphical methods and classical procedures through computationally intensive bootstrap strategies to advanced empirical likelihood techniques. It bridges the gap between theoretical statistical methods and practical procedures applied to the planning and analysis of health-related experiments. The book covers the underpinnings of appropriate statistical inference in addition to new theoretical methods, open problems, and novel testing procedures. It also offers software routines for a majority of the methods based on R and SAS.

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.

Designed for nonstatisticians and statisticians new to the analysis of growth and development data, this book is an accessible and practical guide to a wide range of basic and advanced statistical methods that are useful for studying human growth and development. It collects methods scattered throughout the literature and explains how to use them to solve common research problems. It also discusses how well a method addresses a specific scientific question and how to interpret and present the analytic results. Stata codes are available on the book¿s CRC Press web page.

Health economics is concerned with the study of the cost-effectiveness of health care interventions. This book provides an overview of Bayesian methods for the analysis of health economic data. After an introduction to the basic economic concepts and methods of evaluation, it presents Bayesian statistics using accessible mathematics. The next chapters describe the theory and practice of cost-effectiveness analysis from a statistical viewpoint, and Bayesian computation, notably MCMC. The final chapter presents three detailed case studies covering cost-effectiveness analyses using individual data from clinical trials, evidence synthesis and hierarchical models and Markov models. The text uses WinBUGS and JAGS with datasets and code available online.

Suitable for cancer clinicians and biostatisticians, this book explains how to properly select and accurately use diverse statistical methods for designing and analyzing phase II trials. The author first reviews the statistical methods for single-arm phase II trials since some methodologies for randomized phase II trials stem from single-arm phase II trials and many phase II cancer clinical trials still use single-arm designs. The book then presents methods for randomized phase II trials and describes statistical methods for both single-arm and randomized phase II trials.