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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.

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.

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.

Ageing epidemiology is the study of the health of an ageing population. It is a very active area of research in epidemiology, but has its own issues, which has led to the development of methodology specific to the area. This book covers all the essential statistical tools for analysing cross sectional and longitudinal epidemiological data for ageing research. It has lots of worked examples using real data to illustrate the methods, and Stata and R code for all the examples.

This book will present a unified and up-to date introduction to ROC methodologies, covering both diagnosis (classification) and prediction. The book will emphasize the practical implementation of these methods using standard statistical software such as R and STATA.

This book presents the advanced statistical methods for mapping pharmacogenetic control by integrating pharmacokinetic and pharmacodynamic principles of drug-body interactions. This book is suitable for graduate students and researchers in the field of biology, medicine, bioinformatics and drug design and delivery.

This book focuses on current research and methodologies developed for re-engineering cancer clinical trials and also for "hot" areas in drug development trials such as adaptive design, seamless Phase 2-3 trials, and personalized medicine using biomarkers. Background statistical methodology is summarized in the Appendix.

Presenting an introductory perspective to modern Bayesian procedures, this work explores Bayesian principles and illustrates their application to healthcare research. It focuses on the history and mathematical foundation of Bayesian procedures, before discussing their implementation in healthcare research from first principles.

This book concerns use of real world data (RWD) and real world evidence (RWE) to aid drug development across product cycle. RWD are healthcare data that are collected outside the constraints of conventual controlled randomized trials (CRTs); whereas RWE is the knowledge derived from aggregation and analysis of RWD.

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.

Statistical Topics in Health Economics and Outcomes Research fulfils the need for a volume that presents a coherent and unified review of the major issues that arise in application, especially from a statistical perspective, by presenting an overview of the key analytical issues and best practice.

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.

The main goal of this book is to define 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. The book will be aimed at a broad audience and will emphasize a hands-on approach with a detailed discussion of practical issues arising in clinical trial optimization and R software implementation (relevant statistical methodology will be moved to the appendix).

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.

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.

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.