Böcker i Chapman & Hall/CRC Biostatistics Series-serien

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 provides a comprehensive coverage on safety monitoring methodologies, covering both global trends and regional initiatives. Pharmacovigilance has traditionally focused on the handling of individual adverse event reports however recently there had been a shift towards aggregate analysis to better understand the scope of product risks.

It is critical for non-clinical statisticians to communicate effectively with scientists. Therefore, they must possess the ability to transform scientific questions into statistical hypotheses and models. This book affords new and experienced statisticians with the opportunity to enhance their statistical tools and improve their consulting skills.

Intended to be a single source of information, this book covers a wide range of topics on the changing landscape of drug R&D, emerging applications of big data, AI and machine learning in drug development, and the build of robust data science organizations to drive biopharmaceutical digital transformations.

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not under study, and the feasibility and validation of non-medical switch post-approval.Key Features:Reviews withdrawn draft guidance on analytical similarity assessment.Evaluates various methods for analytical similarity evaluation based on FDA's current guidelines.Provides a general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.

This book shows how model-assisted designs can greatly improve the efficiency and simplify the conduct of early-phase dose finding and optimization trials. It should therefore be a very useful practical reference for biostatisticians, clinicians working in clinical trials, and drug regulatory professionals, as well as grad students.

The subject of this book is applied Bayesian methods for chemistry, manufacturing, and control (CMC) studies in the biopharmaceutical industry. The book has multiple authors from industry and academia, each contributing a case study (chapter), covering a broad array of CMC topics.

A disease is defined as rare if the prevalence is fewer than 200,000 in the United States. It is estimated that there are more than 7,000 rare diseases, which collectively affect 30 million Americans. This diverse and complex disease area poses challenges for patients, caregivers, regulators, drug developers and other stakeholders.

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.

This book is aimed to compile typical fundamental to advanced statistical methods to be used for health data sciences. This book promotes the applications to health and health-related data. The data and computing programs will be available to facilitate readers' learning experience.

Clinical trials require strategic thinking and innovative methods since some traditional methods are not adequate for the 21st century.To solve these problems requires good methodological skills, but also an in-depth knowledge of the practical problems we are dealing with and a strategic vision of the pig picture.

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.

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.

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.

Statistical Methods for Healthcare Performance Monitoring covers measuring quality, types of data, risk adjustment, defining good and bad performance, statistical monitoring, presenting the results to different audiences and evaluating the monitoring system itself. Using examples from around the world, it brings all the issues and

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.

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

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 samp