How to identify optimal phase II trial designs Providing a practical guide containing the information needed to make crucial decisions regarding phase II trial designs, A Practical Guide to Designing Phase II Trials in Oncology sets forth specific points for consideration between the statistician and clinician when designing a phase II trial, including issues such as how the treatment works, choice of outcome measure and randomization, and considering both academic and industry perspectives. A comprehensive and systematic library of available phase II trial designs is included, saving time otherwise spent considering multiple manuscripts, and real-life practical examples of using this approach to design phase II trials in cancer are given. A Practical Guide to Designing Phase II Trials in Oncology: Offers a structured and practical approach to phase II trial design Considers trial design from both an academic and industry perspective Includes a structured library of available phase II trial designs Is relevant to both clinical and statistical researchers at all levels Includes real life examples of applying this approach For those new to trial design, A Practical Guide to Designing Phase II Trials in Oncology will be a unique and practical learning tool, providing an introduction to the concepts behind informed decision making in phase II trials. For more experienced practitioners, the book will offer an overview of new, less familiar approaches to phase II trial design, providing alternative options to those which they may have previously used.

A comprehensive and practical resource for analyses of crossover designs For ethical reasons, it is vital to keep the number of patients in a clinical trial as low as possible. As evidenced by extensive research publications, crossover design can be a useful and powerful tool to reduce the number of patients needed for a parallel group design in studying treatments for non-curable chronic diseases. This book introduces commonly-used and well-established statistical tests and estimators in epidemiology that can easily be applied to hypothesis testing and estimation of the relative treatment effect for various types of data scale in crossover designs. Models with distribution-free random effects are assumed and hence most approaches considered here are semi-parametric. The book provides clinicians and biostatisticians with the exact test procedures and exact interval estimators, which are applicable even when the number of patients in a crossover trial is small. Systematic discussion on sample size determination is also included, which will be a valuable resource for researchers involved in crossover trial design. Key features: Provides exact test procedures and interval estimators, which are especially of use in small-sample cases. Presents most test procedures and interval estimators in closed-forms, enabling readers to calculate them by use of a pocket calculator or commonly-used statistical packages. Each chapter is self-contained, allowing the book to be used a reference resource. Uses real-life examples to illustrate the practical use of test procedures and estimators Provides extensive exercises to help readers appreciate the underlying theory, learn other relevant test procedures and understand how to calculate the required sample size. Crossover Designs: Testing, Estimation and Sample Size will be a useful resource for researchers from biostatistics, as well as pharmaceutical and clinical sciences. It can also be used as a textbook or reference for graduate students studying clinical experiments.

Bayesian Networks “This book should have a place on the bookshelf of every forensic scientist who cares about the science of evidence interpretation.” Dr. Ian Evett, Principal Forensic Services Ltd, London, UK Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science Second Edition Continuing developments in science and technology mean that the amounts of information forensic scientists are able to provide for criminal investigations is ever increasing. The commensurate increase in complexity creates diffculties for scientists and lawyers with regard to evaluation and interpretation, notably with respect to issues of inference and decision. Probability theory, implemented through graphical methods, and specifically Bayesian networks, provides powerful methods to deal with this complexity. Extensions of these methods to elements of decision theory provide further support and assistance to the judicial system. Bayesian Networks for Probabilistic Inference and Decision Analysis in Forensic Science provides a unique and comprehensive introduction to the use of Bayesian decision networks for the evaluation and interpretation of scientific findings in forensic science, and for the support of decision-makers in their scientific and legal tasks. Includes self-contained introductions to probability and decision theory. Develops the characteristics of Bayesian networks, object-oriented Bayesian networks and their extension to decision models. Features implementation of the methodology with reference to commercial and academically available software. Presents standard networks and their extensions that can be easily implemented and that can assist in the reader’s own analysis of real cases. Provides a technique for structuring problems and organizing data based on methods and principles of scientific reasoning. Contains a method for the construction of coherent and defensible arguments for the analysis and evaluation of scientific findings and for decisions based on them. Is written in a lucid style, suitable for forensic scientists and lawyers with minimal mathematical background. Includes a foreword by Ian Evett. The clear and accessible style of this second edition makes this book ideal for all forensic scientists, applied statisticians and graduate students wishing to evaluate forensic findings from the perspective of probability and decision analysis. It will also appeal to lawyers and other scientists and professionals interested in the evaluation and interpretation of forensic findings, including decision making based on scientific information.

An Introduction to clinical trials is a concise step-by-step guide to the principles and practices of clinical trials for those studying clinical trials or new to working on one. Clinical trials are critical to the progress of medicine and improving healthcare, as they evaluate whether new treatments and interventions work. They are also complex, multidisciplinary projects that integrate science, ethics, and legal requirements in the conduct of medical research. Starting with the research question, An Introduction to clinical trials explains study design, sample size determination, study set-up, study conduct, statistical analysis, and dissemination of the results. The book primarily focusses on randomised controlled trials as the "ultimate" clinical trial. It demystifies the terminology used in clinical trials research and presents the underlying scientific and statistical concepts. Real-life examples are used throughout to bring concepts to life. Written by an experienced medical statistician, An Introduction to clinical trials will benefit readers of all backgrounds, from postgraduate and medical students, trainee doctors and healthcare professionals to others working on clinical trials in a professional capacity. This book aims to help readers gain a fuller and more rounded understanding of clinical trials.

Modeling and Analysis of Compositional Data presents a practical and comprehensive introduction to the analysis of compositional data along with numerous examples to illustrate both theory and application of each method. Based upon short courses delivered by the authors, it provides a complete and current compendium of fundamental to advanced methodologies along with exercises at the end of each chapter to improve understanding, as well as data and a solutions manual which is available on an accompanying website. Complementing Pawlowsky-Glahn’s earlier collective text that provides an overview of the state-of-the-art in this field, Modeling and Analysis of Compositional Data fills a gap in the literature for a much-needed manual for teaching, self learning or consulting.

This textbook discusses core principles and practices in gastrointestinal oncology and covers a wide range of practice areas such as pathology and radiologic images, epidemiology, genetics, staging, multidisciplinary management of specific gastrointestinal cancer, and pathology for each primary tumor site. The comprehensive coverage makes Textbook of Gastrointestinal Oncology a useful resource for the practitioner wishing to gain a greater understanding of the principles of managing malignant gastrointestinal disease, as well as medical oncology fellows, surgeons, radiation oncologists, gastroenterologists and fellows, and residents.

This book illustrates numerous statistical practices that are commonly used by medical researchers, but which have severe flaws that may not be obvious. For each example, it provides one or more alternative statistical methods that avoid misleading or incorrect inferences being made. The technical level is kept to a minimum to make the book accessible to non-statisticians. At the same time, since many of the examples describe methods used routinely by medical statisticians with formal statistical training, the book appeals to a broad readership in the medical research community.

This is a comprehensive major reference work for our SpringerReference program covering clinical trials. Although the core of the Work will focus on the design, analysis, and interpretation of scientific data from clinical trials, a broad spectrum of clinical trial application areas will be covered in detail. This is an important time to develop such a Work, as drug safety and efficacy emphasizes the Clinical Trials process. Because of an immense and growing international disease burden, pharmaceutical and biotechnology companies continue to develop new drugs. Clinical trials have also become extremely globalized in the past 15 years, with over 225,000 international trials ongoing at this point in time. Principles in Practice of Clinical Trials is truly an interdisciplinary that will be divided into the following areas: 1) Clinical Trials Basic Perspectives 2) Regulation and Oversight 3) Basic Trial Designs 4) Advanced Trial Designs 5) Analysis 6) Trial Publication 7) Topics Related Specific Populations and Legal Aspects of Clinical Trials The Work is designed to be comprised of 175 chapters and approximately 2500 pages. The Work will be oriented like many of our SpringerReference Handbooks, presenting detailed and comprehensive expository chapters on broad subjects. The Editors are major figures in the field of clinical trials, and both have written textbooks on the topic. There will also be a slate of 7-8 renowned associate editors that will edit individual sections of the Reference.

This book focuses on important decision points and evidence needed for making decisions at these points during the development of a new drug. It takes a holistic approach towards drug development by incorporating explicitly knowledge learned from the earlier part of the development and available historical information into decisions at later stages. In addition, the book shares lessons learned from several select examples published in the literature since the publication of the first edition. The second edition reiterates the need for making evidence-based Go/No Go decisions in drug development discussed in the first edition. It substantially expands several topics that have seen great advances since the publication of the first edition. The most noticeable additions include three adaptive trials conducted in recent years that offer excellent learning opportunities, the use of historical data in the design and analysis of clinical trials, and extending decision criteria to the cases when the primary endpoint is binary. The examples used to illustrate the additional materials all come from real trials with some post-trial reflections offered by the authors. The book begins with an overview of product development and regulatory approval pathways. It then discusses how to incorporate prior knowledge into study design and decision making at different stages of drug development. Prior knowledge includes information pertaining to historical controls. To assist decision making, the book discusses appropriate metrics and the formulation of go/no-go decisions for progressing a drug candidate to the next development stage. Using the concept of the positive predictive value in the field of diagnostics, the book leads readers to the assessment of the probability that an investigational product is effective given positive study outcomes. Lastly, the book points out common mistakes made by drug developers under the current drug-development paradigm. The book offers useful insights to statisticians, clinicians, regulatory affairs managers and decision-makers in the pharmaceutical industry who have a basic understanding of the drug-development process and the clinical trials conducted to support drug-marketing authorization. The authors provide software codes for select analytical approaches discussed in the book. The book includes enough technical details to allow statisticians to replicate the quantitative illustrations so that they can generate information to facilitate decision-making themselves.

There is an increasing need for educational resources for statisticians and investigators. Reflecting this, the goal of this book is to provide readers with a sound foundation in the statistical design, conduct, and analysis of clinical trials. Furthermore, it is intended as a guide for statisticians and investigators with minimal clinical trial experience who are interested in pursuing a career in this area. The advancement in genetic and molecular technologies have revolutionized drug development. In recent years, clinical trials have become increasingly sophisticated as they incorporate genomic studies, and efficient designs (such as basket and umbrella trials) have permeated the field. This book offers the requisite background and expert guidance for the innovative statistical design and analysis of clinical trials in oncology. Key Features: Cutting-edge topics with appropriate technical background Built around case studies which give the work a "hands-on" approach Real examples of flaws in previously reported clinical trials and how to avoid them Access to statistical code on the book’s website Chapters written by internationally recognized statisticians from academia and pharmaceutical companies Carefully edited to ensure consistency in style, level, and approach Topics covered include innovating phase I and II designs, trials in immune-oncology and rare diseases, among many others