Binbing Yu

Kristine Broglio

Drug development is a strictly regulated area. As such, marketing approval of a new drug depends heavily, if not exclusively, on evidence generated from clinical trials. Drug development has seen tremendous innovation in science and technology that has revolutionized the treatment of some diseases. And yet, the statistical design and practical conduct of the clinical trials used to test new therapeutics for safety and efficacy have changed very little over the decades. Our approach to clinical trials is steeped in convention and tradition. The large, fixed, randomized controlled trial methods that have been the gold standard are well understood and expected by many trial stakeholders. However, this approach is not well suited to all aspects of modern drug development and the current competitive landscape. We now see new therapies that target a small fraction of the patient population, rare diseases with high unmet medical needs, and pediatric populations that must wait for years for new drug approvals from the time that therapies are approved in adults. Large randomized clinical trials are at best inefficient and at worst completely infeasible in many modern clinical settings. Advances in technology and data infrastructure call for innovations in clinical trial design. Despite advances in statistical methods, the availability of information, and computing power, the actual experience with innovative design in clinical trials across industry and academia is limited. This book will be an important showcase of the potential for these innovative designs in modern drug development and will be an important resource to guide those who wish to undertake them for themselves. This book is ideal for professionals in the pharmaceutical industry and regulatory agencies, but it will also be useful to academic researchers, faculty members, and graduate students in statistics, biostatistics, public health, and epidemiology due to its focus on innovation. Key Features: Is written by pharmaceutical industry experts, academic researchers, and regulatory reviewers; this is the first book providing a comprehensive set of case studies related to statistical methodology, implementation, regulatory considerations, and communication of complex innovative trial design. Has a broad appeal to a multitude of readers across academia, industry, and regulatory agencies. Each contribution is a practical case study that can speak to the benefits of an innovative approach but also balance that with the real-life challenges encountered. A complete understanding of what is actually being done in modern clinical trials will broaden the reader’s capabilities and provide examples to first mimic and then customize and expand upon when exploring these ideas on their own.

Case Studies in Innovative Clinical Trials

Harry Yang

Real-world evidence (RWE) has been at the forefront of pharmaceutical innovations. It plays an important role in transforming drug development from a process aimed at meeting regulatory expectations to an operating model that leverages data from disparate sources to aid business, regulatory, and healthcare decision making. Despite its many benefits, there is no single book systematically covering the latest development in the field. Written specifically for pharmaceutical practitioners, Real-World Evidence in Drug Development and Evaluation, presents a wide range of RWE applications throughout the lifecycle of drug product development. With contributions from experienced researchers in the pharmaceutical industry, the book discusses at length RWE opportunities, challenges, and solutions. Features Provides the first book and a single source of information on RWE in drug development Covers a broad array of topics on outcomes- and value-based RWE assessments Demonstrates proper Bayesian application and causal inference for real-world data (RWD) Presents real-world use cases to illustrate the use of advanced analytics and statistical methods to generate insights Offers a balanced discussion of practical RWE issues at hand and technical solutions suitable for practitioners with limited data science expertise

Real-World Evidence in Drug Development and Evaluation

Wei Zhao

Jianchun Zhang

Develop Effective Immunogenicity Risk Mitigation StrategiesImmunogenicity assessment is a prerequisite for the successful development of biopharmaceuticals, including safety and efficacy evaluation. Using advanced statistical methods in the study design and analysis stages is therefore essential to immunogenicity risk assessment and mitigation stra

Statistical Methods for Immunogenicity Assessment

Yingwei Peng

Cure Models: Methods, Applications and Implementation is the first book in the last 25 years that provides a comprehensive and systematic introduction to the basics of modern cure models, including estimation, inference, and software. This book is useful for statistical researchers and graduate students, and practitioners in other disciplines to have a thorough review of modern cure model methodology and to seek appropriate cure models in applications. The prerequisites of this book include some basic knowledge of statistical modeling, survival models, and R and SAS for data analysis. The book features real-world examples from clinical trials and population-based studies and a detailed introduction to R packages, SAS macros, and WinBUGS programs to fit some cure models. The main topics covered include the foundation of statistical estimation and inference of cure models for independent and right-censored survival data, cure modeling for multivariate, recurrent-event, and competing-risks survival data, and joint modeling with longitudinal data, statistical testing for the existence and difference of cure rates and sufficient follow-up, new developments in Bayesian cure models, applications of cure models in public health research and clinical trials.

Binbing Yu

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