Quantitative Decisions in Drug Development - Christy Chuang-Stein

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Résumé :
This book offers a high-level treatise of evidence-based decisions in drug development. Because of the inseparable relationship between designs and decisions, a good portion of this book is devoted to the design of clinical trials. The book begins with an overview of product development and regulatory approval pathways. It then discusses how to incorporate prior knowledge into study design and decision making at different stages of drug development. The latter include selecting appropriate metrics to formulate decisions criteria, determining go/no-go decisions for progressing a drug candidate to the next stage and predicting the effectiveness of a product. Lastly, it points out common mistakes made by drug developers under the current drug-development paradigm. The book offers useful insights to statisticians, clinicians, regulatory affairs managers and decision-makers in the pharmaceutical industry who have a basic understanding of the drug-development processand the clinical trials conducted to support drug-marketing authorization. The authors provide software codes for select analytical approaches discussed in the book. The book includes enough technical details to allow statisticians to replicate the quantitative illustrations so that they can generate information to facilitate decision-making themselves....

Biographie:
Christy Chuang-Stein received a bachelor degree in mathematics from the National Taiwan University and a PhD in statistics from the University of Minnesota. She retired from Pfizer as Vice President and Head of the Statistical Research and Consulting Center in July 2015, after 30 years in the pharmaceutical industry and 5 years in academia (University of Rochester). Currently, Christy is the owner and Principal Consultant of Chuang-Stein Consulting, LLC and consults broadly in the areas of pharmaceutical development and evaluation. Christy is a Fellow of the American Statistical Association (ASA) and received the ASA's Founders' Award in 2012. She was the recipient of the Distinguished Achievement Award of the International Chinese Statistical Association in 2013 and the Distinguished Service Award from the National Institute of Statistical Sciences in 2020. She is also a repeat recipient of the Drug Information Association's Donald Francke Award for Excellence in Journal Publishing and the Thomas Teal Award for Excellence in Statistics Publishing. Christy is a founding editor of the journal Pharmaceutical Statistics. Simon Kirby received a BSc In Economics and Economic Policy from Loughborough University, an MSc in Statistics from the University of Kent, a PhD in Statistics from the University of Edinburgh and a BA in Mathematics from the Open University. He retired from Pfizer in 2018 after almost 20 years working as a Principal Statistician, Clinical Statistics Head, Therapeutic Area Statistics Head and Consultant in the Statistical Research and Consulting Center. He is the owner of SKSTATS Limited for which he does occasional statistical consultancy. Simon is a Fellow and Chartered Statistician of the Royal Statistical Society. He previously worked as a Lecturer, Senior Lecturer then Principal Lecturer in Statistics at Liverpool John Moores University and as a Statistician at the U.K.'s Institute of Food Research,Rothamsted Experimental Station and Revlon Healthcare. ...

Sommaire:
Clinical Testing of a New Drug.- A Frequentist Decision-making Framework.- Characteristics of a Diagnostic Test.- The Parallel Between Clinical Trials and Diagnostic Tests.- Incorporating Information from Completed Trials in Future Trial Planning.- Choosing Metrics Appropriate for Different Stages of Drug Development.- Designing Proof-of-Concept Trials with Desired Characteristics.- Designing Dose-response Studies with Desired Characteristics.- Designing Confirmatory Trials with Desired Characteristics.- Designing Phase 4 Trials.- Other Metrics That Have Been Proposed to Optimize Drug Development Decisions.- Discounting Prior Results to Account for Selection Bias.- Index.- Appendix.