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Innovative Strategies, Statistical Solutions and Simulations for Modern Clinical Trials
Chapman & Hall/CRC Biostatistics Series
Genres:
Clinical trials, Epidemiology & medical statistics, Pharmaceutical technology, Biology, life sciences, Probability & statistics
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Synopsis
"This is truly an outstanding book. [It] brings together all of the latest research in clinical trials methodology and how it can be applied to drug development.... Chang et al provide applications to industry-supported trials. This will allow statisticians in the industry community to take these methods seriously." Jay Herson, Johns Hopkins University
The pharmaceutical industry's approach to drug discovery and development has rapidly transformed in the last decade from the more traditional Research and Development (R & D) approach to a more innovative approach in which strategies are employed to compress and optimize the clinical development plan and associated timelines. However, these strategies are generally being considered on an individual trial basis and not as part of a fully integrated overall development program. Such optimization at the trial level is somewhat near-sighted and does not ensure cost, time, or development efficiency of the overall program. This book seeks to address this imbalance by establishing a statistical framework for overall/global clinical development optimization and providing tactics and techniques to support such optimization, including clinical trial simulations.
Provides a statistical framework for achieve global optimization in each phase of the drug development process.
Describes specific techniques to support optimization including adaptive designs, precision medicine, survival-endpoints, dose finding and multiple testing.
Gives practical approaches to handling missing data in clinical trials using SAS.
Looks at key controversial issues from both a clinical and statistical perspective.
Presents a generous number of case studies from multiple therapeutic areas that help motivate and illustrate the statistical methods introduced in the book.
Puts great emphasis on software implementation of the statistical methods with multiple examples of software code (both SAS and R).
It is important for statisticians to possess a deep knowledge of the drug development process beyond statistical considerations. For these reasons, this book incorporates both statistical and "clinical/medical" perspectives. New & Used
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What Reviewers Are Saying
"This is the first edition of a comprehensive book covering the most recent methodology on innovative clinical trial designs for drugs and biological products. It is a great reference book for statisticians, clinicians, and other stakeholders involved in drug discovery and development. ... Chang et al aimed to provide the statistical framework to reach the overall development program optimizations in this book. In addition, innovative methodology to mitigate the risks of failed efficacy, safety, strategy, commercial and operation failures have been described by Chang et al. Special techniques such as clinical trial simulations are highly recommended by the authors. ....
In summary, this is an excellent reference book for statisticians, clinicians, and all stakeholders involved in clinical development program with a common goal to reach clinical development optimization."
-Holly Huang in the Journal of Biopharmaceutical Statistics, October 2019
"The book has a number of detailed examples, and SAS and R code to implement some of the methods described in the text...In summary, this book covers a wide range of interesting topics in clinical trials, and provides an appealing and useful reference to researchers."
- Ionut Bebu, JASA 2020
"This first edition of this book provides the most recent methodology and statistical considerations in the design and management of clinical trials. It is focused on professionals in drug development, specifically statisticians and clinical researchers...It is, however, a useful insight into strategies for specific situations including personalized medicine, missing data, adaptive design, and multiple testing. The authors include a large number of practical clinical trials from various therapeutic areas, and they put emphasis on the use of clinical trial simulations...The authors present a remarkable amount of SAS code examples that could be directly used in daily practice. An extensive overview of modern innovative strategies for clinical trials helps to broaden the horizons of scientists interested in drug or treatment development."
- Iveta Selingerova, ISCB News, July 2020 "This is the first edition of a comprehensive book covering the most recent methodology on innovative clinical trial designs for drugs and biological products. It is a great reference book for statisticians, clinicians, and other stakeholders involved in drug discovery and development. ...Chang et al aimed to provide the statistical framework to reach the overall development program optimizations in this book. In addition, innovative methodology to mitigate the risks of failed efficacy, safety, strategy, commercial and operation failures have been described by Chang et al. Special techniques such as clinical trial simulations are highly recommended by the authors. ....
In summary, this is an excellent reference book for statisticians, clinicians, and all stakeholders involved in clinical development program with a common goal to reach clinical development optimization."
-Holly Huang in the Journal of Biopharmaceutical Statistics, October 2019
In summary, this is an excellent reference book for statistician
-Holly Huang in the Journal of Biopharmaceu
"The book has a number of detailed examples, and SAS and R code to implement some of the methods described in the text...In summary, this book covers a wide range of interesting topics in clinical trials, and provides an appealing and useful reference to researchers.
- Ionut Bebu, JASA 2020
"This first edition of this book provides the most recent methodology and statistical consideratio
- Iveta Selingerova,
In summary, this is an excellent reference book for statistician
-Holly Huang in the Journal of Biopharmaceu