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Unlimited
Authorized users
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Document Delivery Supplied
Can lend chapters, not whole ebooks
Title
Quantitative decisions in drug development / Christy Chuang-Stein, Simon Kirby.
Edition
Second edition.
ISBN
9783030797317 (electronic bk.)
3030797317 (electronic bk.)
9783030797300
3030797309
Published
Cham : Springer, [2021]
Copyright
©2021
Language
English
Description
1 online resource : illustrations (some color)
Other Standard Identifiers
10.1007/978-3-030-79731-7 doi
Call Number
RM301.25 .C58 2021
Dewey Decimal Classification
615.1/9
Summary
This book focuses on important decision points and evidence needed for making decisions at these points during the development of a new drug. It takes a holistic approach towards drug development by incorporating explicitly knowledge learned from the earlier part of the development and available historical information into decisions at later stages. In addition, the book shares lessons learned from several select examples published in the literature since the publication of the first edition. The second edition reiterates the need for making evidence-based Go/No Go decisions in drug development discussed in the first edition. It substantially expands several topics that have seen great advances since the publication of the first edition. The most noticeable additions include three adaptive trials conducted in recent years that offer excellent learning opportunities, the use of historical data in the design and analysis of clinical trials, and extending decision criteria to the cases when the primary endpoint is binary. The examples used to illustrate the additional materials all come from real trials with some post-trial reflections offered by the authors. 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. Prior knowledge includes information pertaining to historical controls. To assist decision making, the book discusses appropriate metrics and the formulation of go/no-go decisions for progressing a drug candidate to the next development stage. Using the concept of the positive predictive value in the field of diagnostics, the book leads readers to the assessment of the probability that an investigational product is effective given positive study outcomes. Lastly, the book 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 process and 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.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed September 16, 2021).
Series
Springer series in pharmaceutical statistics. 2366-8709
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.
