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

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

"This is truly an outstanding book. [It] brings together all of the latest research in clinical trials methodology and how it can be applied to drug development.... Chang et al provide applications to industry-supported trials. This will allow statisticians in the industry community to take these methods seriously." Jay Herson, Johns Hopkins University The pharmaceutical industry's approach to drug discovery and development has rapidly transformed in the last decade from the more traditional Research and Development (R & D) approach to a more innovative approach in which strategies are employed to compress and optimize the clinical development plan and associated timelines. However, these strategies are generally being considered on an individual trial basis and not as part of a fully integrated overall development program. Such optimization at the trial level is somewhat near-sighted and does not ensure cost, time, or development efficiency of the overall program. This book seeks to address this imbalance by establishing a statistical framework for overall/global clinical development optimization and providing tactics and techniques to support such optimization, including clinical trial simulations. Provides a statistical framework for achieve global optimization in each phase of the drug development process. Describes specific techniques to support optimization including adaptive designs, precision medicine, survival-endpoints, dose finding and multiple testing. Gives practical approaches to handling missing data in clinical trials using SAS. Looks at key controversial issues from both a clinical and statistical perspective. Presents a generous number of case studies from multiple therapeutic areas that help motivate and illustrate the statistical methods introduced in the book. Puts great emphasis on software implementation of the statistical methods with multiple examples of software code (both SAS and R). It is important for statisticians to possess a deep knowledge of the drug development process beyond statistical considerations. For these reasons, this book incorporates both statistical and "clinical/medical" perspectives.

Randomised Clinical Trials: Design, Practice and Reporting provides a detailed overview of the methodology for conducting clinical trials, including developing protocols, data capture, randomisation, analysis and reporting. Assuming no prior background, this user-friendly resource describes the statistical, regulatory, and practical components required for conducting randomised clinical trials. Numerous examples and case studies from industry, academia, and the research literature help readers understand each stage of the clinical trial process. This second edition contains extensively revised material throughout, including new chapters covering designs for repeated measures, non-inferiority, cluster and stepped wedge trials. Other new chapters describe data and safety monitoring, biomarker studies, and feasibility studies. Updated and expanded sections discuss situations where multiple organs, different body locations or competing risks are involved, subgroup analysis, and multiple outcomes. Written by an author team with extensive experience in conducting clinical trials, this book: Provides comprehensive coverage of randomised clinical trials, ranging from basic to advanced Features several new chapters, updated case studies and examples, and references to changes in regulations Explains basic randomised trials, including the parallel two-group controlled trial with a single outcome measure Covers paired trial designs and trials with more than two interventions Includes a chapter on miscellaneous topics such as adaptive designs, large simple trials, Bayesian methods for very small trials, alpha-spending functions and the predictive probability test Randomised Clinical Trials is essential reading for clinicians, nurses, data managers, and medical statisticians involved in clinical trials, and for health practitioners responsible for direct patient care in a clinical trial setting.

The original edition of this text, Clinical Evaluation of Medical Devices: Principles and Case Studies, provided the first overview of key pr- ciples and approaches to medical device clinical trials, illustrated with a series of detailed, real-world case studies. The book is designed as a resource for clinical professionals and regulatory specialists working in the field of new medical device development and marketing. Since the first edition of this text was published in 1997, the rapid pace of inno- tion in health care technologies continues to yield exciting and important new products. The regulatory landscape has also evolved, reflecting some of the changes and needs within the medical device industry. The purpose of Clinical Evaluation of Medical Devices: Principles and Case Studies, Second Edition is to provide an updated and expanded presentation of the scientific methods and regulatory requirements applied to the study of new significant risk medical devices. The text now includes (1) new information on the requirements and process for gaining reimbursement of new products from Medicare and private insurers, with case studies of research specifically designed for this p- pose as well as health care technology assessment methods; (2) infor- tion on new statistical methodologies applied to medical device trials; and (3) all new case studies, including examples of combination pr- ucts, three-phase development models (i. e. , feasibility, FDA approval, Medicare reimbursement), and novel study designs.

Clinical trials are used to elucidate the most appropriate preventive, diagnostic, or treatment options for individuals with a given medical condition. Perhaps the most essential feature of a clinical trial is that it aims to use results based on a limited sample of research participants to see if the intervention is safe and effective or if it is comparable to a comparison treatment. Sample size is a crucial component of any clinical trial. A trial with a small number of research participants is more prone to variability and carries a considerable risk of failing to demonstrate the effectiveness of a given intervention when one really is present. This may occur in phase I (safety and pharmacologic profiles), II (pilot efficacy evaluation), and III (extensive assessment of safety and efficacy) trials. Although phase I and II studies may have smaller sample sizes, they usually have adequate statistical power, which is the committee's definition of a "large" trial. Sometimes a trial with eight participants may have adequate statistical power, statistical power being the probability of rejecting the null hypothesis when the hypothesis is false. Small Clinical Trials assesses the current methodologies and the appropriate situations for the conduct of clinical trials with small sample sizes. This report assesses the published literature on various strategies such as (1) meta-analysis to combine disparate information from several studies including Bayesian techniques as in the confidence profile method and (2) other alternatives such as assessing therapeutic results in a single treated population (e.g., astronauts) by sequentially measuring whether the intervention is falling above or below a preestablished probability outcome range and meeting predesigned specifications as opposed to incremental improvement.

This book provides insights into how to be a productive clinical researcher via real-life case examples of successful clinical research -- and also clinical research gone awry. Through these examples of success and failure, the book develops a blueprint for building a career in clinical research. Future medical practice depends on the quality of the clinical trials to which drugs, devices, and treatment procedures are subjected today. However, clinical trials are not easy to do, and many physicians and health care providers who attempt clinical research struggle in this endeavor, primarily because of lack of instruction. Clinical Research aims to fill the gap between training and research through case studies of a long-time clinical researcher’s rich and varied experiences.