Christy Chuang-Stein, Simon Kirby

Quantitative Decisions in Drug Development (eBook, PDF)

• Format: PDF

Produktbeschreibung

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.

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Produktdetails

• Verlag: Springer International Publishing
• Seitenzahl: 248
• Erscheinungstermin: 25. Mai 2017
• Englisch
• ISBN-13: 9783319460765
• Artikelnr.: 53037005

Autorenporträt

Christy Chuang-Stein was Vice President and Head of the Statistical Research and Consulting Center at Pfizer prior to retirement from the company in July 2015. She has more than 30 years of experience in the pharmaceutical industry and 160 scientific publications. She is a Fellow of the American Statistical Association (ASA) and received ASA's Founders' Award in 2012. She was the recipient of the Distinguished Achievement Award of the International Chinese Statistical Association in 2013.

Simon Kirby is Senior Director at the Statistical Research and Consulting Center at Pfizer. He has worked for Pfizer for more than 17 years after previously holding the position of Principal Lecturer in Statistics at Liverpool John Moores University. He has also previously worked as a Statistician at the Institute of Food Research in the UK, Rothamsted and Revlon Healthcare.

Inhaltsangabe

Chapter 1 - Clinical Testing of a New Drug.- Chapter 2 - A Frequentist Decision-making Framework.- Chapter 3 - Characteristics of a Diagnostic Test.- Chapter 4 - The Parallel Between Clinical Trials and Diagnostic Tests.- Chapter 5 - Incorporating Information from Completed Trials in Future Trial Planning.- Chapter 6 - Choosing Metrics Appropriate for Different Stages of Drug Development.- Chapter 7 - Designing Proof-of-Concept Trials with Desired Characteristics.- Chapter 8 - Designing Dose-response Studies with Desired Characteristics.- Chapter 9 - Designing Confirmatory Trials with Desired Characteristics.- Chapter 10 - Designing Phase 4 Trials.- Chapter 11 - Other Metrics That Have Been Proposed to Optimize Drug Development Decisions.- Chapter 12 - Discounting Prior Results to Account for Selection Bias.- Chapter 13 - Adaptive Designs.- Chapter 14 - Additional Topics.

Rezensionen

"This work offers useful algorithms, classifications, and other general points to statisticians or 'quantitative scientists'. But, it is also really useful to regulatory affairs managers, clinicians, medical writers, and all kinds of decision-makers in the industry." (Andrei Myslivets, ISCB News, Vol. 68, December, 2019)
"It is presented in a concise, structured, friendly, and illustrative way that allows for a good understanding of the underlying ideas ... . the book from Chuang-Stein and Kirby is a valuable, interesting and easy read for statisticians and clinicians with some methodological background who are involved in clinical development or drug approval and who are looking for a structured way to make clinical development decisions." (Norbert Benda, Biometrical Journal, Vol. 61 (4), July, 2016)