Quantitative Decisions in Drug Development

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.

Focuses on important decision points and evidence needed for making decisions at these points during the development of a new drug Takes a holistic approach towards drug development by incorporating knowledge learned from the earlier part of the development explicitly into the decisions at later stages Shows the parallel between clinical trials and diagnostic tests and how this analogy is used to emphasize the importance of replication in drug development Describes how to incorporate prior knowledge into study design and decision making at different stages of drug development Explains metrics useful to address the objectives of the different stages of drug development and how to compare design options based on these metrics Demonstrates why over-estimation is a common problem in drug development and how adjustment should be considered to correct the over-estimation

Autorentext

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.

Inhalt
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.