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Main Data
Editor: Liang Fang, Cheng Su
Title: Statistical Methods in Biomarker and Early Clinical Development
Publisher: Springer-Verlag
ISBN/ISSN: 9783030315030
Edition: 1
Price: CHF 110.00
Publication date: 01/01/2019
Content
Category: Mathematics
Language: English
Technical Data
Pages: 348
Kopierschutz: DRM
Geräte: PC/MAC/eReader/Tablet
Formate: PDF
Table of contents
This contributed volume offers a much-needed overview of the statistical methods in early clinical drug and biomarker development. Chapters are written by expert statisticians with extensive experience in the pharmaceutical industry and regulatory agencies. Because of this, the data presented is often accompanied by real world case studies, which will help make examples more tangible for readers. The many applications of statistics in drug development are covered in detail, making this volume a must-have reference.

Biomarker development and early clinical development are the two critical areas on which the book focuses. By having the two sections of the book dedicated to each of these topics, readers will have a more complete understanding of how applying statistical methods to early drug development can help identify the right drug for the right patient at the right dose. Also presented are exciting applications of machine learning and statistical modeling, along with innovative methods and state-of-the-art advances, making this a timely and practical resource.

This volume is ideal for statisticians, researchers, and professionals interested in pharmaceutical research and development. Readers should be familiar with the fundamentals of statistics and clinical trials.


Liang Fang is an Executive Director and Head of Biostatistics in MyoKardia Inc. His research interests include statistical applications in drug development, precision medicine, and digital health.

Cheng Su is an Executive Director of Data Science & Analytics at BioMarin, Inc. His research interests include statistical applications and tool development in drug discovery, biomarkers, clinical trials design, risk based monitoring, mobile health and big data.