Books in the Chapman & Hall/CRC Biostatistics Series series

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.

Analyses the data in health sciences and policy by introducing meta-analysis strategies while reviewing commonly used techniques. This text provides various chapters that build on principles, develop methodologies to solve statistical problems, and present concrete applications used by experienced medical practitioners and health policymakers.

A practical guide for biomedical researchers, clinicians, biostatisticians, and graduate students in biostatistics, this volume covers the latest developments in the analysis and modeling of interval-censored time-to-event data. Top researchers from academia, biopharmaceutical industries, and government agencies show how up-to-date statistical methods are used in biopharmaceutical and public health applications. The book presents data from actual clinical trials and biomedical research, including breast cancer and HIV data sets. It also offers easy access to computational methods and R software packages.

This book explains how to determine sample size for studies with correlated outcomes, which are widely implemented in medical, epidemiological, and behavioral studies. For clustered studies, the authors provide sample size formulas that account for variable cluster sizes and within-cluster correlation. For longitudinal studies, they present sample size formulas that account for within-subject correlation among repeated measurements and various missing data patterns. For multiple levels of clustering, the authors describe how randomization impacts trial administration, analysis, and sample size requirement.

Describes how to conceptualize, perform, and critique traditional generalized linear models (GLMs) from a Bayesian perspective and how to use modern computational methods to summarize inferences using simulation. Introducing dynamic modeling for GLMs, this work considers parametric and semiparametric approaches to overdispersed GLMs.

This book illustrates the use of effect size measures and corresponding confidence intervals as more informative alternatives to the most basic and widely used significance tests. It provides you with a deep understanding of what happens when these statistical methods are applied in situations far removed from the familiar Gaussian case. Requiring little computational skills, the book offers user-friendly Excel spreadsheets for download at www.crcpress.com, enabling you to easily apply the methods to your own empirical data.

Taking into account the International Conference Harmonisation E5 framework for bridging studies, this book covers the regulatory requirements, scientific and practical issues, and statistical methodology for designing and evaluating bridging studies and multiregional clinical trials. For bridging studies, the authors explore ethnic sensitivity, the necessity of bridging studies, types of bridging studies, and the assessment of similarity between regions based on bridging evidence. For multiregional clinical trials, the text considers regional differences, assesses the consistency of treatment effect across regions, and discusses sample size determination for each region.

This acclaimed book covers the principles and methodologies in adaptive design and analysis that pertain to adaptations made to trial or statistical procedures based on accrued data of ongoing clinical trials. It presents a well-balanced summary of current regulatory perspectives, recently developed statistical methods, and statistical tests for seamless phase II/III adaptive designs. This edition features two new chapters as well as a complete rewrite of the chapter on computer simulation. It also includes computer simulations and various case studies to ensure a practical understanding of the methodologies.

From simple NLMs to complex GLMMs, this book describes how to use the GUI for WinBUGS - BugsXLA - an Excel add-in written by the author that allows a range of Bayesian models to be easily specified.

An important method for statistical validation is the receiver operating characteristic (ROC) analysis. This visual tool is used in a variety of clinical areas, including laboratory testing, epidemiology, radiology, and bioinformatics, for evaluating diagnostic tests. This book gives a historical overview of the empirical and nonparametric ROC method for continuous diagnostic and classification data. It introduces methods for estimating and comparing ROC curves based on diagnostic test results and covers both semiparametric and parametric models. The authors develop likelihood-based algorithms for estimating an ROC curve and its characteristics under these models. They also present methods for sample size calculations and Monte Carlo simulations. The text includes many real clinical examples, with R code provided for all of them.

After a review of the usual measures, including specificity, sensitivity, positive predictive value, negative predictive value, and the area under the ROC curve, this book expands its scope to cover the more advanced topics of verification bias, diagnostic tests with imperfect gold standards, and medical tests where no gold standard is available. The author offers a practical treatment by including R and WinBUGS code in the examples and by employing the Bayesian approach throughout the text. He also provides practical problems at the end of each chapter.

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.

Discusses the development and application of Bayesian statistical methods for the analysis of high-throughput bioinformatics data arising from problems in molecular and structural biology and disease-related medical research, such as cancer. This book focuses on design, statistical inference, and data analysis from a Bayesian perspective.

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.

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.

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.

Explores the growing role of Bayesian thinking in the rapidly changing world of clinical trial analysis. This book summarizes the state of clinical trial design and analysis and introduces the main ideas and potential benefits of a Bayesian alternative.

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.

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.

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.