Statistical Thinking for Non-Statisticians in Drug Regulation

Author: Richard Kay

Publisher: John Wiley & Sons


Category: Medical

Page: 296

View: 257

Written by a well-known lecturer and consultant to thepharmaceutical industry, this book focuses on the pharmaceuticalnon-statistician working within a very strict regulatoryenvironment. Statistical Thinking for Clinical Trials inDrug Regulation presents the concepts and statisticalthinking behind medical studies with a direct connection to theregulatory environment so that readers can be clear where thestatistical methodology fits in with industry requirements.Pharmaceutical-related examples are used throughout to set theinformation in context. As a result, this book provides adetailed overview of the statistical aspects of the design,conduct, analysis and presentation of data from clinical trialswithin drug regulation. Statistical Thinking for Clinical Trials in DrugRegulation: Assists pharmaceutical personnel in communicating effectivelywith statisticians using statistical language Improves the ability to read and understand statisticalmethodology in papers and reports and to critically appraisethat methodology Helps to understand the statistical aspects of the regulatoryframework better quoting extensively from regulatory guidelinesissued by the EMEA (European Medicines Evaluation Agency), ICH(International Committee on Harmonization and the FDA (Food andDrug Administration)

Cardiovascular Safety in Drug Development and Therapeutic Use

New Methodologies and Evolving Regulatory Landscapes

Author: J. Rick Turner

Publisher: Springer


Category: Medical

Page: 342

View: 730

At a time when the field of cardiac safety is going through important changes, this unique book provides the rationale for, and cutting-edge explanations of, new regulatory landscapes that will likely govern cardiac safety assessments globally for the foreseeable future. Exposure-response modeling is already being accepted by regulatory agencies in lieu of the traditional Thorough QT/QTc Study, and the Comprehensive in vitro Proarrhythmia Assay initiative is well under way. Developments in the field of cardiovascular safety are also described and discussed in the book. These include the search for more efficient ways to exonerate new drugs for type 2 diabetes from an unacceptable cardiovascular liability, how best to address off-target blood pressure increases induced by noncardiovascular drugs, and the continued evolution of the discipline of Cardio-oncology. “a resource that will likely serve as a standard for years to come” - Dr Jonathan Seltzer Therapeutic Innovation & Regulatory Science, 2017;51(2):180 “I have no hesitation in recommending this book as a valuable reference source” - Dr Rashmi Shah Journal for Clinical Studies, 2017;9(1):62-63

Pharmaceutical Statistics Using SAS

A Practical Guide

Author: Alex Dmitrienko

Publisher: SAS Institute


Category: Computers

Page: 464

View: 188

Introduces a range of data analysis problems encountered in drug development and illustrates them using case studies from actual pre-clinical experiments and clinical studies. Includes a discussion of methodological issues, practical advice from subject matter experts, and review of relevant regulatory guidelines.

Nonclinical Statistics for Pharmaceutical and Biotechnology Industries

Author: Lanju Zhang

Publisher: Springer


Category: Medical

Page: 698

View: 997

This book serves as a reference text for regulatory, industry and academic statisticians and also a handy manual for entry level Statisticians. Additionally it aims to stimulate academic interest in the field of Nonclinical Statistics and promote this as an important discipline in its own right. This text brings together for the first time in a single volume a comprehensive survey of methods important to the nonclinical science areas within the pharmaceutical and biotechnology industries. Specifically the Discovery and Translational sciences, the Safety/Toxiology sciences, and the Chemistry, Manufacturing and Controls sciences. Drug discovery and development is a long and costly process. Most decisions in the drug development process are made with incomplete information. The data is rife with uncertainties and hence risky by nature. This is therefore the purview of Statistics. As such, this book aims to introduce readers to important statistical thinking and its application in these nonclinical areas. The chapters provide as appropriate, a scientific background to the topic, relevant regulatory guidance, current statistical practice, and further research directions.

Clinical Trial Methodology

Author: Karl E. Peace

Publisher: CRC Press


Category: Mathematics

Page: 420

View: 543

Now viewed as its own scientific discipline, clinical trial methodology encompasses the methods required for the protection of participants in a clinical trial and the methods necessary to provide a valid inference about the objective of the trial. Drawing from the authors’ courses on the subject as well as the first author’s more than 30 years working in the pharmaceutical industry, Clinical Trial Methodology emphasizes the importance of statistical thinking in clinical research and presents the methodology as a key component of clinical research. From ethical issues and sample size considerations to adaptive design procedures and statistical analysis, the book first covers the methodology that spans every clinical trial regardless of the area of application. Crucial to the generic drug industry, bioequivalence clinical trials are then discussed. The authors describe a parallel bioequivalence clinical trial of six formulations incorporating group sequential procedures that permit sample size re-estimation. The final chapters incorporate real-world case studies of clinical trials from the authors’ own experiences. These examples include a landmark Phase III clinical trial involving the treatment of duodenal ulcers and Phase III clinical trials that contributed to the first drug approved for the treatment of Alzheimer’s disease. Aided by the U.S. FDA, the U.S. National Institutes of Health, the pharmaceutical industry, and academia, the area of clinical trial methodology has evolved over the last six decades into a scientific discipline. This guide explores the processes essential for developing and conducting a quality clinical trial protocol and providing quality data collection, biostatistical analyses, and a clinical study report, all while maintaining the highest standards of ethics and excellence.

Theory of Drug Development

Author: Eric B. Holmgren

Publisher: CRC Press


Category: Mathematics

Page: 261

View: 676

Theory of Drug Development presents a formal quantitative framework for understanding drug development that goes beyond simply describing the properties of the statistics in individual studies. It examines the drug development process from the perspectives of drug companies and regulatory agencies. By quantifying various ideas underlying drug development, the book shows how to systematically address problems, such as: Sizing a phase 2 trial and choosing the range of p-values that will trigger a follow-up phase 3 trial Deciding whether a drug should receive marketing approval based on its phase 2/3 development program and recent experience with other drugs in the same clinical area Determining the impact of adaptive designs on the quality of drugs that receive marketing approval Designing a phase 3 pivotal study that permits the data-driven adjustment of the treatment effect estimate Knowing when enough information has been gathered to show that a drug improves the survival time for the whole patient population Drawing on his extensive work as a statistician in the pharmaceutical industry, the author focuses on the efficient development of drugs and the quantification of evidence in drug development. He provides a rationale for underpowered phase 2 trials based on the notion of efficiency, which leads to the identification of an admissible family of phase 2 designs. He also develops a framework for evaluating the strength of evidence generated by clinical trials. This approach is based on the ratio of power to type 1 error and transcends typical Bayesian and frequentist statistical analyses.