Böcker i Chapman & Hall/CRC Biostatistics Series-serien

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.

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.

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.

This text gives graduate students with diverse backgrounds across the health, medical, social, and mathematical sciences a solid, unified foundation in the principles of statistical inference. Drawing on his extensive experience teaching graduate-level biostatistics courses and working in the pharmaceutical industry, the author covers the theoretical underpinnings essential to understanding subsequent core methodologies in the field. Extended examples illustrate key concepts in depth using a specific biostatistical context and simple R functions are provided for conducting simulation studies.

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.

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.

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.

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.

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.

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.

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.

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.