Bücher der Reihe Chapman & Hall/CRC Biostatistics Series

One of the hallmarks of the 21st century medicine is the emergence of digital therapeutics (DTx)-evidence-based, clinically validated digital technologies to prevent, diagnose, treat, and manage various diseases and medical conditions. DTx solutions have been gaining interest from patients, investors, healthcare providers, health authorities, and other stakeholders because of the potential of DTx to deliver equitable, massively scalable, personalized and transformative treatments for different unmet medical needs.Digital Therapeutics: Scientific, Statistical, Clinical, and Regulatory Aspects is an unparalleled summary of the current scientific, statistical, developmental, and regulatory aspects of DTx which is poised to become the fastest growing area of the biopharmaceutical and digital medicine product development. This edited volume intends to provide a systematic exposition to digital therapeutics through 19 peer-reviewed chapters written by subject matter experts in this emerging field.This edited volume is an invaluable resource for business leaders and researchers working in public health, healthcare, digital health, information technology, and biopharmaceutical industries. It will be also useful for regulatory scientists involved in the review of DTx products, and for faculty and students involved in an interdisciplinary research on digital health and digital medicine.Key Features:Provides the taxonomy of the concepts and a navigation tool for the field of DTx.Covers important strategic aspects of the DTx industry, thereby helping investors, developers, and regulators gain a better appreciation of the potential value of DTx.Expounds on many existing and emerging state-of-the art scientific and technological tools, as well as data privacy, ethical and regulatory considerations for DTx product development.Presents several case studies of successful development of some of the most remarkable DTx products.Provides some perspectives and forward-looking statements on the future of digital medicine.

Drug development sponsors cannot run individual trials for all products in all indications. This results in missed opportunities. Clinical trial programs known as "basket" and "umbrella" have demonstrated that multi-product, multi-indication trials can be efficient and feasible.

Exploring applications in medical research and clinical epidemiology, this text continues to cover important issues related to the design and analysis of reliability and agreement studies. This edition contains a new chapter that describes various models for methods comparison studies as well as a new chapter on the analysis of reproducibility u

The increased use of non-inferiority analysis has been accompanied by a proliferation of research on the design and analysis of non-inferiority studies. Design and Analysis of Non-Inferiority Trials brings together this body of research and confronts the issues involved in the design of a non-inferiority trial. Using examples from real clinical tri

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. Requiri

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 clinic

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 book provides the first comprehensive account of the self-controlled case series (SCCS) method, a statistical method for investigating associations between outcome events and time-varying exposures.

Methodologies for Biosimilar Product Development covers the practical and challenging issues that are commonly encountered during the development, review, and approval of a proposed biosimilar product. These practical and challenging issues include, but are not limited to the mix-up use of interval hypotheses testing (i.e., the use of TOST) and confidence interval approach, a risk/benefit assessment for non-inferiority/similarity margin, PK/PD bridging studies with multiple references, the detection of possible reference product change over time, design and analysis of biosimilar switching studies, the assessment of sensitivity index for assessment of extrapolation across indications without collecting data from those indications not under study, and the feasibility and validation of non-medical switch post-approval.Key Features:Reviews withdrawn draft guidance on analytical similarity assessment.Evaluates various methods for analytical similarity evaluation based on FDA's current guidelines.Provides a general approach for the use of n-of-1 trial design for assessment of interchangeability.Discusses the feasibility and validity of the non-medical switch studies.Provides innovative thinking for detection of possible reference product change over time.This book embraces innovative thinking of design and analysis for biosimilar studies, which are required for review and approval of biosimilar regulatory submissions.

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 is focused on the critical clinical initiatives introduced by the 21st Century Cure Act passed by the United States Congress in December 2016. The book covers everything from the outline of the initiatives to analysis on the effect on biopharmaceutical research and development.

This book contains chapters providing interpretations of principles in ICH E17 and new ideas of implementing MRCTs. Authors are from different regions, and from academia and industry. This book will be of particular interest to biostatisticians working in late stage clinical development of medical products.

Methodologies in Biosimilar Product Development covers the practical and challenging issues that are commonly encountered during the development, review, and approval of a proposed biosimilar product.

This volume 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. It presents a broad overview of statistical inference, clustering, and classification problems in two main high-throughput platforms: microarray gene expression and phylogenic analysis. Illustrating concepts using real-world data, the book covers a variety of recently developed Bayesian techniques, along with applications in genome-wide studies, phylogenetics, breast cancer, expression genomics, and more.

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.

Encyclopedic in breadth, yet practical and concise, Medical Biostatistics, Fourth Edition focuses on the statistical aspects ofmedicine with a medical perspective, showing the utility of biostatistics as a tool to manage many medical uncertainties. This edition includes more topics in order to fill gaps in the previous edition.

-A CRC title, part of the Taylor & Francis imprint, a member of the Taylor & Francis Group, the academic division of T&F Informa plc.-

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.

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.

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 book summarizes the author¿s experience in serving on many data monitoring committees 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 -sponsored trials.

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.